{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR10 VQ-VAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Define the transformations to apply to the data\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\n",
    "])\n",
    "\n",
    "# Load the CIFAR10 train dataset\n",
    "train_set = torchvision.datasets.CIFAR10(root='~/data', train=True, download=True, transform=transform)\n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True, num_workers=2)\n",
    "\n",
    "# Load the CIFAR10 test dataset\n",
    "test_set = torchvision.datasets.CIFAR10(root='~/data', train=False, download=True, transform=transform)\n",
    "test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False, num_workers=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the encoder architecture\n",
    "class Encoder(nn.Module):\n",
    "    def __init__(self, latent_dim):\n",
    "        super(Encoder, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1)\n",
    "        self.conv2 = nn.Conv2d(64, 128, kernel_size=4, stride=2, padding=1)\n",
    "        self.conv3 = nn.Conv2d(128, 256, kernel_size=4, stride=2, padding=1)\n",
    "        self.conv4 = nn.Conv2d(256, latent_dim, kernel_size=4, stride=2, padding=1)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.relu(self.conv1(x))\n",
    "        x = self.relu(self.conv2(x))\n",
    "        x = self.relu(self.conv3(x))\n",
    "        x = self.conv4(x)\n",
    "        return x\n",
    "\n",
    "# Define the decoder architecture\n",
    "class Decoder(nn.Module):\n",
    "    def __init__(self, latent_dim):\n",
    "        super(Decoder, self).__init__()\n",
    "        self.deconv1 = nn.ConvTranspose2d(latent_dim, 256, kernel_size=4, stride=2, padding=1)\n",
    "        self.deconv2 = nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1)\n",
    "        self.deconv3 = nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1)\n",
    "        self.deconv4 = nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.tanh = nn.Tanh()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.relu(self.deconv1(x))\n",
    "        x = self.relu(self.deconv2(x))\n",
    "        x = self.relu(self.deconv3(x))\n",
    "        x = self.deconv4(x)\n",
    "        x = self.tanh(x)\n",
    "        return x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Quantizer(nn.Module):\n",
    "    def __init__(self, num_embeddings, embedding_dim):\n",
    "        super(Quantizer, self).__init__()\n",
    "        self.embedding = nn.Embedding(num_embeddings, embedding_dim)\n",
    "        # w/o this line, the model will not learn\n",
    "        self.embedding.weight.data.uniform_(-1./num_embeddings, 1./num_embeddings)\n",
    "        self.embedding_dim = embedding_dim\n",
    "        self.num_embeddings = num_embeddings\n",
    "        \n",
    "    def forward(self, z_e):\n",
    "        # z_e - encoder latent code (B, C, H, W) -> (B, H, W, C)\n",
    "        z_e = z_e.permute(0, 2, 3, 1).contiguous()\n",
    "        z_e_shape = z_e.shape\n",
    "        # flat version of z_e\n",
    "        flat_z_e = z_e.view(-1, self.embedding_dim)\n",
    "\n",
    "        # distance between z_e and embeddings\n",
    "        distances = (torch.sum(flat_z_e**2, dim=1, keepdim=True) \n",
    "                    + torch.sum(self.embedding.weight**2, dim=1)\n",
    "                    - 2 * torch.matmul(flat_z_e, self.embedding.weight.t()))\n",
    "        \n",
    "        # minimal distance between z_e and embeddings\n",
    "        encoding_indices = torch.argmin(distances, dim=1).unsqueeze(1)\n",
    "        encodings = torch.zeros(encoding_indices.shape[0], self.num_embeddings, device=z_e.device)\n",
    "        # one-hot vector based on the minimal distance\n",
    "        encodings.scatter_(1, encoding_indices, 1)\n",
    "        \n",
    "        # Quantize and unflatten\n",
    "        # N one-hot encodings quantizing/filtering by 0 or 1 each dim (col) of embedding\n",
    "        # The encodings builds a map of which feature point of each feature embedding vector is used\n",
    "        z_q = torch.matmul(encodings, self.embedding.weight).view(z_e_shape)\n",
    "        \n",
    "        # What is the purpose of z_q + (z_e - z_q).detach()?\n",
    "        z_q = z_e + (z_q - z_e).detach()\n",
    "        z_q = z_q.permute(0, 3, 1, 2).contiguous()\n",
    "\n",
    "        return z_q, encoding_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VQVAE(nn.Module):\n",
    "    def __init__(self, latent_dim, num_embeddings, embedding_dim):\n",
    "        super(VQVAE, self).__init__()\n",
    "        self.encoder = Encoder(latent_dim)\n",
    "        self.decoder = Decoder(latent_dim)\n",
    "        self.quantizer = Quantizer(num_embeddings, embedding_dim)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        z_e = self.encoder(x)\n",
    "        z_q, encoding_indices = self.quantizer(z_e)\n",
    "        x_hat = self.decoder(z_q)\n",
    "        return x_hat, z_e, z_q, encoding_indices\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_dim = 2048\n",
    "num_embeddings = 512\n",
    "embedding_dim = 16\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = VQVAE(latent_dim, num_embeddings, embedding_dim).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 2048, 2, 2]) torch.Size([512, 1])\n",
      "torch.Size([1, 3, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "random_input = torch.randn(1, 3, 32, 32).to(device)\n",
    "z_e = model.encoder(random_input)\n",
    "z_q, indeces = model.quantizer(z_e)\n",
    "print(z_q.shape, indeces.shape)\n",
    "x_hat = model.decoder(z_q)\n",
    "print(x_hat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of parameters: 18105231\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 1/50 [00:04<03:38,  4.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/50], Ave Loss: 8.0734, Recon Loss: 0.0019, Commit Loss: 6.4572, Latent Loss: 6.4572\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "  4%|▍         | 2/50 [00:09<03:39,  4.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [2/50], Ave Loss: 0.6572, Recon Loss: 0.0009, Commit Loss: 0.5251, Latent Loss: 0.5251\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  6%|▌         | 3/50 [00:13<03:32,  4.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [3/50], Ave Loss: 0.3447, Recon Loss: 0.0007, Commit Loss: 0.2752, Latent Loss: 0.2752\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 4/50 [00:18<03:27,  4.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [4/50], Ave Loss: 0.2560, Recon Loss: 0.0006, Commit Loss: 0.2043, Latent Loss: 0.2043\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 10%|█         | 5/50 [00:22<03:22,  4.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [5/50], Ave Loss: 0.2495, Recon Loss: 0.0006, Commit Loss: 0.1991, Latent Loss: 0.1991\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 12%|█▏        | 6/50 [00:27<03:19,  4.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [6/50], Ave Loss: 0.2721, Recon Loss: 0.0005, Commit Loss: 0.2173, Latent Loss: 0.2173\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▍        | 7/50 [00:31<03:14,  4.53s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [7/50], Ave Loss: 0.2741, Recon Loss: 0.0005, Commit Loss: 0.2189, Latent Loss: 0.2189\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 16%|█▌        | 8/50 [00:36<03:10,  4.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [8/50], Ave Loss: 0.2983, Recon Loss: 0.0004, Commit Loss: 0.2383, Latent Loss: 0.2383\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|█▊        | 9/50 [00:40<03:05,  4.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [9/50], Ave Loss: 0.3154, Recon Loss: 0.0004, Commit Loss: 0.2520, Latent Loss: 0.2520\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 20%|██        | 10/50 [00:45<03:01,  4.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [10/50], Ave Loss: 0.3212, Recon Loss: 0.0004, Commit Loss: 0.2567, Latent Loss: 0.2567\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 11/50 [00:49<02:58,  4.57s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [11/50], Ave Loss: 0.3368, Recon Loss: 0.0004, Commit Loss: 0.2691, Latent Loss: 0.2691\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██▍       | 12/50 [00:54<02:52,  4.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [12/50], Ave Loss: 0.3502, Recon Loss: 0.0003, Commit Loss: 0.2799, Latent Loss: 0.2799\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 26%|██▌       | 13/50 [00:58<02:48,  4.54s/it]"
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    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch [13/50], Ave Loss: 0.3433, Recon Loss: 0.0003, Commit Loss: 0.2744, Latent Loss: 0.2744\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|██▊       | 14/50 [01:03<02:43,  4.54s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [14/50], Ave Loss: 0.3355, Recon Loss: 0.0003, Commit Loss: 0.2681, Latent Loss: 0.2681\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 30%|███       | 15/50 [01:07<02:38,  4.53s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [15/50], Ave Loss: 0.3349, Recon Loss: 0.0003, Commit Loss: 0.2677, Latent Loss: 0.2677\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32%|███▏      | 16/50 [01:12<02:34,  4.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [16/50], Ave Loss: 0.3263, Recon Loss: 0.0003, Commit Loss: 0.2608, Latent Loss: 0.2608\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 34%|███▍      | 17/50 [01:16<02:28,  4.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [17/50], Ave Loss: 0.3238, Recon Loss: 0.0003, Commit Loss: 0.2588, Latent Loss: 0.2588\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 36%|███▌      | 18/50 [01:21<02:25,  4.55s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [18/50], Ave Loss: 0.3307, Recon Loss: 0.0003, Commit Loss: 0.2644, Latent Loss: 0.2644\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|███▊      | 19/50 [01:26<02:21,  4.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [19/50], Ave Loss: 0.3474, Recon Loss: 0.0003, Commit Loss: 0.2777, Latent Loss: 0.2777\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 40%|████      | 20/50 [01:30<02:15,  4.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [20/50], Ave Loss: 0.3516, Recon Loss: 0.0003, Commit Loss: 0.2811, Latent Loss: 0.2811\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████▏     | 21/50 [01:35<02:10,  4.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [21/50], Ave Loss: 0.3580, Recon Loss: 0.0003, Commit Loss: 0.2862, Latent Loss: 0.2862\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 44%|████▍     | 22/50 [01:39<02:06,  4.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [22/50], Ave Loss: 0.3629, Recon Loss: 0.0003, Commit Loss: 0.2901, Latent Loss: 0.2901\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 46%|████▌     | 23/50 [01:44<02:01,  4.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [23/50], Ave Loss: 0.3748, Recon Loss: 0.0003, Commit Loss: 0.2996, Latent Loss: 0.2996\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 48%|████▊     | 24/50 [01:48<01:57,  4.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [24/50], Ave Loss: 0.3823, Recon Loss: 0.0003, Commit Loss: 0.3056, Latent Loss: 0.3056\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 25/50 [01:53<01:52,  4.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [25/50], Ave Loss: 0.3806, Recon Loss: 0.0003, Commit Loss: 0.3043, Latent Loss: 0.3043\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 52%|█████▏    | 26/50 [01:57<01:49,  4.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [26/50], Ave Loss: 0.3798, Recon Loss: 0.0002, Commit Loss: 0.3036, Latent Loss: 0.3036\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 54%|█████▍    | 27/50 [02:02<01:45,  4.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [27/50], Ave Loss: 0.3828, Recon Loss: 0.0002, Commit Loss: 0.3061, Latent Loss: 0.3061\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 56%|█████▌    | 28/50 [02:06<01:40,  4.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [28/50], Ave Loss: 0.3799, Recon Loss: 0.0002, Commit Loss: 0.3037, Latent Loss: 0.3037\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 29/50 [02:11<01:35,  4.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [29/50], Ave Loss: 0.3810, Recon Loss: 0.0002, Commit Loss: 0.3046, Latent Loss: 0.3046\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 60%|██████    | 30/50 [02:15<01:30,  4.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [30/50], Ave Loss: 0.3841, Recon Loss: 0.0002, Commit Loss: 0.3071, Latent Loss: 0.3071\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 62%|██████▏   | 31/50 [02:20<01:25,  4.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [31/50], Ave Loss: 0.3859, Recon Loss: 0.0002, Commit Loss: 0.3085, Latent Loss: 0.3085\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 64%|██████▍   | 32/50 [02:24<01:20,  4.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [32/50], Ave Loss: 0.3877, Recon Loss: 0.0002, Commit Loss: 0.3100, Latent Loss: 0.3100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 66%|██████▌   | 33/50 [02:29<01:17,  4.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [33/50], Ave Loss: 0.4061, Recon Loss: 0.0002, Commit Loss: 0.3247, Latent Loss: 0.3247\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|██████▊   | 34/50 [02:34<01:12,  4.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [34/50], Ave Loss: 0.4308, Recon Loss: 0.0002, Commit Loss: 0.3444, Latent Loss: 0.3444\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 70%|███████   | 35/50 [02:38<01:07,  4.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [35/50], Ave Loss: 0.4325, Recon Loss: 0.0002, Commit Loss: 0.3458, Latent Loss: 0.3458\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 72%|███████▏  | 36/50 [02:43<01:03,  4.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [36/50], Ave Loss: 0.4417, Recon Loss: 0.0002, Commit Loss: 0.3532, Latent Loss: 0.3532\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 74%|███████▍  | 37/50 [02:47<00:58,  4.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [37/50], Ave Loss: 0.4338, Recon Loss: 0.0002, Commit Loss: 0.3469, Latent Loss: 0.3469\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 76%|███████▌  | 38/50 [02:51<00:53,  4.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [38/50], Ave Loss: 0.4366, Recon Loss: 0.0002, Commit Loss: 0.3491, Latent Loss: 0.3491\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 78%|███████▊  | 39/50 [02:56<00:49,  4.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [39/50], Ave Loss: 0.4424, Recon Loss: 0.0002, Commit Loss: 0.3537, Latent Loss: 0.3537\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 80%|████████  | 40/50 [03:01<00:45,  4.55s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [40/50], Ave Loss: 0.4494, Recon Loss: 0.0002, Commit Loss: 0.3594, Latent Loss: 0.3594\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|████████▏ | 41/50 [03:05<00:40,  4.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [41/50], Ave Loss: 0.4514, Recon Loss: 0.0002, Commit Loss: 0.3609, Latent Loss: 0.3609\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 84%|████████▍ | 42/50 [03:10<00:36,  4.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [42/50], Ave Loss: 0.4504, Recon Loss: 0.0002, Commit Loss: 0.3601, Latent Loss: 0.3601\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████▌ | 43/50 [03:14<00:31,  4.51s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [43/50], Ave Loss: 0.4501, Recon Loss: 0.0002, Commit Loss: 0.3599, Latent Loss: 0.3599\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 88%|████████▊ | 44/50 [03:19<00:26,  4.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [44/50], Ave Loss: 0.4476, Recon Loss: 0.0002, Commit Loss: 0.3579, Latent Loss: 0.3579\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|█████████ | 45/50 [03:23<00:22,  4.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [45/50], Ave Loss: 0.4432, Recon Loss: 0.0002, Commit Loss: 0.3544, Latent Loss: 0.3544\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 46/50 [03:28<00:17,  4.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [46/50], Ave Loss: 0.4448, Recon Loss: 0.0002, Commit Loss: 0.3557, Latent Loss: 0.3557\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 94%|█████████▍| 47/50 [03:32<00:13,  4.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [47/50], Ave Loss: 0.4455, Recon Loss: 0.0002, Commit Loss: 0.3563, Latent Loss: 0.3563\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 96%|█████████▌| 48/50 [03:37<00:09,  4.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [48/50], Ave Loss: 0.4566, Recon Loss: 0.0002, Commit Loss: 0.3651, Latent Loss: 0.3651\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 49/50 [03:41<00:04,  4.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [49/50], Ave Loss: 0.4753, Recon Loss: 0.0002, Commit Loss: 0.3801, Latent Loss: 0.3801\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 50/50 [03:46<00:00,  4.52s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [50/50], Ave Loss: 0.4722, Recon Loss: 0.0002, Commit Loss: 0.3776, Latent Loss: 0.3776\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Set the number of epochs\n",
    "num_epochs = 50\n",
    "\n",
    "# Create an optimizer\n",
    "optimizer = optim.Adam(model.parameters())\n",
    "# Print the number of parameters\n",
    "print(f\"Number of parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad)}\")\n",
    "\n",
    "# Train the model\n",
    "for epoch in tqdm(range(num_epochs)):\n",
    "    total_loss = 0\n",
    "    r_loss = 0\n",
    "    c_loss = 0\n",
    "    l_loss = 0  \n",
    "    total = 0 \n",
    "    for batch_idx, (data, _) in enumerate(train_loader):\n",
    "        # Move the data to the device\n",
    "        data = data.to(device)\n",
    "        total += data.size(0)\n",
    "        \n",
    "        # Forward pass\n",
    "        x_hat, z_e, z_q, indices = model(data)\n",
    "        recon_loss = nn.MSELoss()(x_hat, data)\n",
    "        commit_loss = nn.MSELoss()(z_e.detach(), z_q)\n",
    "        latent_loss = nn.MSELoss()(z_e, z_q.detach())\n",
    "        loss = recon_loss + 0.25 * commit_loss + latent_loss\n",
    "        \n",
    "        # Backward pass\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        total_loss += loss.item()\n",
    "        r_loss += recon_loss.item()\n",
    "        c_loss += commit_loss.item()\n",
    "        l_loss += latent_loss.item()\n",
    "    \n",
    "    tqdm.write(f\"Epoch [{epoch+1}/{num_epochs}], Ave Loss: {total_loss/total:.4f}, Recon Loss: {r_loss/total:.4f}, Commit Loss: {c_loss/total:.4f}, Latent Loss: {l_loss/total:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H82qNfTgcQr3f7yttPp9D2xHYQyIiaZpCHbWDfm9VrBxjvpgqzfPwHgpdfEaKkQMFbe3rTow3hiPY7/Kl9o1hrwdt54ZvOD7ut1PqIFZn2Harsw/12Nd9GZfG+WnE0qCt96eISBesQyVGzAI5jYhIWmBfdxvdjrU/W0YeikLtYqnPTxGRqNWBehzhsTeO9ofCyHFX5bXrm1AftrUvdGO8N0YzvObvnOB9/+hc676Dz5ftCPtN2QDd8PXAyLMtv0E5tWvEBRGjDV+vl+saiYNxXjZWogFiUWnlE65x3TR/0/gDxGVSGOv3jD9wjX/7h9IQcC1ame4mzjOyTMeUyRzH3ukH+nwREWlqnDt7IL8NjfgThni/xJG273Txvuj28LmznE+gLrke5wsvPGO0bdxTat2GazyJmHHdyCeQnqX4DJjP8NqcGrnXZKLnZDLF8f5siu+Wheg5+eQb/wa0DdvPQL1tpkdgM34M+2I6O4D63oa+k7eX+HybJ0+gvrON84y8PlTa2egxtLXemSpwN7PuOmGI++2i+7uIxOCu3m7hvWW/tegzZ9e4Szx3+wWov/Odb0J9d6jzjzjG8aMy3sEc4+xC746+McbKyJXzQu/z8Qjvw81tfK+3DqMCxOsyx7FpFRwH52dolr0a97UycgnrfdNr9JoYzzVyc0vf00VE3nj2utJiIwcIfNzvn/4R/Kby3pneX+89OIa24wafie2W3s8v7eN3o2dv38T9ePce1L/+Pf1W5ffw3u91cFwxUmJ4b+gGeM91Ary3OiBvjQPjDlni+XONtQzBBQb500Xh/9QghBBCCCGEEEIIIYQQQshawI8ahBBCCCGEEEIIIYQQQghZC/hRgxBCCCGEEEIIIYQQQgghawE/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwFuIw6IFkuoN40uqK56+JvJSmowi4ikmYp1FutWGm+h7tcGZXVy6LU/WsqaOuHUBbXw2XlPTdSmuME0NZxcLV5z6+1rfGpyfNxG47g/lVoHYyxlKWepx+E71/YdcQB1e1FRDwPjweD1/cvGtebQL05eai04uQI2p6fjqE+X+B9kaaZ0pK0gLZFqf3rI13Ppx9j3/VDPPeWT7u+3hd1hfdcNjmFOggrph9V6RLq+XwGdSly3Tb6QRFx232oe1EL6k2t+xh7uO0v/9RXoF4lOtZ+71vfhrZJjtfd+EmpGh0TarnMPrwY1lpd5rwwMcbmgsC52dG+KCISLPB5Np1pn/GN/r3++utQD0N8kGxvbyvt7OwM2lrz1+12lXbt2jVou7m5ean+JUmitL29vUv1LwhwDFku9R7NMh3HROyzBf3mxsYGtLX6bZ05ea5jwmXOuIsy6ON40m3rNSlKHP/rBsd0KfCapLleV7+LbV0jFsTtttLCWOdnIiJOiveWJNg38qn+zTzD+9ZIFcWPdByMOlegbeDosYiI+AFuPPDHSis9vAYLB+dRTWXkp5XeA6GRljvGv0GqS72WZYrP2yLB+Uq7g2NFM9B+mWXGuboioxqv+ehcz1FR4D6czrDvjUrsq3Wg42nbyJHD2tiPIC41AR6L5+L9lSSG34A7UxDhfWTl8I6j/ck37hKlkS8iH/uof1ozXE8aF++BujHaBrnU0wDlIB+Bf88663DbP0SH/gxCIy/3wo7SKg9PflLiHNlrcCwMRftv0OC4VGT4N9O59sfRXJ+zIiLNE7yf68bIETx9pk3fvQttoxjHzaLS+zk2zobJKY6bZ8f4Tnd6dKK08ewY2qYpbns+m0J9NNJ3phLc50REgvjibwPn57iNOPwlqPde2IF6PwZB4WPYGItEz7GISDU8V1q/vQ9tZ+MnUN/bwrnD+Uj73W/8478Pbb/6hx9AvQFnQCNW7MbrF0X4fElT7dMor/+oDSOueFoPW/i+++yzz0F9fw/n5dd29f76ztuPoG1RYJ8JjTuGHxkPeIC6wuccuh8kxl1xPNZ+JiISRziGZBO9/3Pj7WIVPMfYa+CctXclnsvGxX7qgul0jfehN27fhPrNvvaxbIrnPgjwvtjHUy9f+MQtpe0OsPH58QHU33j1WaX98hfwG0DbOIdr4073P7yvY/rxyDj7RsacGPfCGPh06OMzMY7wmg1a2h/6QBMRiYw8D+9akRC86SPtovB/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/KhBCCGEEEIIIYQQQgghhJC1gB81CCGEEEIIIYQQQgghhBCyFvCjBiGEEEIIIYQQQgghhBBC1oILlxgvixzqeVEoralxFXbHxd9Q/AB3I8t1dfowxBXXxcFV2xvQv7aPK9Mn+QTqp+MjqHdaW0rr96/i/rkRlD1f9zvw8XwEAa4f77t47KXrKK2qcGV6Eaz7Rl/iOL5w/xxH98PWsa3hOmL1u2m0Xht+uQre4gzq1eGB0hZn2HY2nmI9NfZcqX0ae7SIa6yf51x465v7uTJiQpkutO1yDm0LvNzSgHUNoh62LTPcvySBel6WSrP2kFvhmY3A3hIR8aOWbiNPoe3tl5+Hervzy0prajzXd99+B+pugONkluuxp7kVE354rDgdRToOWraVMfeWr9fJUml7/c6F+yEiMk31Wi0X2p9/UP8ODw9x21O9z61+oPgqInL79m2l7e7uQlsrdpfA/0VwPLZiOoqvIvZ4PE/vr1ZL7xWrHyIiLjgErLFY/bD6jfpi5horsDUYGn+ifal08J4/MvIRr8Zr1XK7uh/xJrTNjJg+melYmjbY1p/jdZ2d4jidjMG6LvGZ07h4X7ietg91+igiIu0+9i8vxvulvzVUmj/AMcHz8Zy0YjwnTaP913GxTzcV9ukSHKJdkJuKiDTGv2PKSrwvlktwhjp4HVfluw9PoF4k+vcqkDeLiDQ+3gNW+tg4etxljeeidnArKDetjbhUiBF7Pdx24GmftGJbYSTJLshVrLtY4OB91+3ifRe2df8OR3gds9I6z3FfGiPHglzC1DgCrKuE3TZoqGku0ZELkhj3g8rR83lYPYG2D2cfQr0T4vxoJ9pX2s3hM9A2amPfqMGENoKDsnHkS23sl8rTMeG8xnn2IsH3+tTX+aKb4TYqcOcSEcmNfZS19DizBO/bOsBnQ9ngMyYv9R614lvg4HgYgvD56L2vQ9vfN/K01uAXoP7KtQ2llQvsw+0OPqcuQjvGvrtI7ymt13oZ2h4evg/1977/21D/Z//8D5WWLvF5eO3KDtQ/uP9IaZUYbzjGHQPl0yL43jCZ4Lm3At7Ghs4NA+M+ubu7h1s2ctGzsfbd1gC3seHpvFVEpDb2IhrPYoHfHVzjvI1ivW+LHOd0Y+M9Z/sKjnFL8B6xOD+GtqvgezgeF7X2pbzB571f41zYFzz3yJO2uthnXn9eny0iIjV473K7OpaIiDQO7kc2P4f6T35Sv7X8xCdvQNvFUt+xRUTe+OynlfbsBn6TWhzfh/qdDP9m/w+0/VGqzycRkRrkrCIieYN9ug70ubMwkqBqgef1wUyfRY7gveUbeXJsHPKhr+3j4IfPo/g/NQghhBBCCCGEEEIIIYQQshbwowYhhBBCCCGEEEIIIYQQQtYCftQghBBCCCGEEEIIIYQQQshawI8ahBBCCCGEEEIIIYQQQghZCy5cLdg3CvChQp2ZVSTPKPDjGkWPUCFWq4CqVVw0AEVVwhYuYPP4gS7uLCIyWY6gHoFCovMFLpLnBbiolQfmtbYqXBoFYtwcj72qQRFKow2r8JQ1r6jotlWE1QK1bRWM+8tKcvc9qDcjXbBoAgoFi4icnI+hHobY131QPLIAxeM+wijOAwoAO0bxTc9YE7fC+zw713ugMQrQWi7jOrov0cAoCtwbQL0yiuSVoKB0q4WLkhWgcLSIiBfhIk6+p38TFU4XEQkNfWtbF8d648tfhLZnJ7hY2egY6z7Y5+0OXvdVsIqZbmzgwl8Iq0D9osDnSJXotWrmeP0KowCfCwKQVRA8NwrInZ6eQv3RI10g8ObNm9B2NptBHfXFirsJKEwnYsd062xFWL9pzQn6TasQ93KJ9xbC8rO33noL6uPxGOqf+tSnlIbOuFVpjDid59pP4x6en8jHuUSe433R6w6V5pY49Sum2GeWT/SaTCbYR50cx93pCBfUrHLdlyDABfj8CPtdA87ETPBcV5WRKwZ4TsZH+tx2N3DsDnbwGdUa4LbLUu+XJMPzGgo+oxwX7QEc0/u9IdSLGu/b2RwU0wVF2Z8Go6MHUA9A8eX+/jVomxhFCgOjKGoF5NooUJ0a1yUP5VjGFFmx1zUKDuegncKI63ala9AP4/cisI9ERGpwl/joD/R4OjGOyQ0oBCoiUlvFtdFcmUM04gLSjN+7bJ1wWCjcsl2Bx/e+DfWopc+BhYMLpZ5PcAHxmVEA2OnokfRdHH9CBxeSRlvR8n+LwMP98x3dlyrCvrsMcaHwJ95YaV4Lx83ONr6Td5xdqIe51gfz69C2nhk5qnUWn+rxzIyixcsJ1sejI6U5gvfnO3+ii2OLiLQHOJfv/9RPKO3w7h9D25+58jNQvwitAM/ndK7v5PkS9/U3f+sdqL//wWOoP3P1WaUlXZwL/I+/8w2oTybavtPGufdlc/vZTOcqx0d472cZbqPb66OOQNudbVzo/ZXXPwP1/mCotOHGNrT97vfehvqHd3Fx99lYv9Oh90kRMR+a0Dlcl9b9D7e96OlC6yIiFXjPaW/hIumr4Hbxe0hdaL+rjWLUpY9zdc8o4Bwt9by98TzO0Z7Zxv0bfaiLpjtDHP83d4GPiojk+I1t+kTv82Ef5yl3rmF/vL6r7VsD3I/uxh2o3zDOysHWt5QWnOH96Tp4TgorRwvAWxp4/xMRcRt8zqF7vXVPT419sbT+D0Wm763OCpkU/6cGIYQQQgghhBBCCCGEEELWAn7UIIQQQgghhBBCCCGEEELIWsCPGoQQQgghhBBCCCGEEEIIWQv4UYMQQgghhBBCCCGEEEIIIWsBP2oQQgghhBBCCCGEEEIIIWQt8C9q2IpjqLuuAzT8raRJcdtliaulZ1l2sc6JiAf6ISLS7uoq746D223HuKp8XnagfnR0prSdnQG09esa6k6hq8rXFTSVpsFtVJWuHi8i4rq66n0Q4CWPjfWtjX6jNXMcvAaWP1zGFvnZR7+J22ma5sK/uQrpk8dQDzvaZ46Px9C2afCY25FePxGRpABrYow3yZa4f42euEDwZBblAupVgtsu80RpWaY1EREvwH4Xxn2lNYs5tPUNn2lHuO3xfKzbBvMhIuK6EdTrBAezOuopzWnhdXQavNFdsJbXn70NbVv7N6A+OxpDvQ3mu7e5AW1XwYrdUaTnszDifxjieFz7OIb5ovfF6XyG+1fhuUcxrDJs3377bai/+OKLUL9586bSgkCfTyIi8zn29ffff19paE5F7JhugcbpG3NtYZ0BSLfOFmtOUEzPc31+iojs7u5CvQPisgj2Ncv/VmE2nUI9L3QsbVwdS0RE2q6OjSIipYt9ZgTi3UCG0PboQ20rInL2wYnSwhSvU1HjeJwaiQ3KU+oGx49CjPwq1OvqOdh3S8eI3YLXOxC9DoVO/UREpD3APh36OEebg/jUGPPU7uH5ThPddlbgfMBP8ZyEsbFvXd2XLMfxelVO774L9Re29druXMH7+MQfQt11cIz0Gz0ftYvXqsHHuLiN9hvHSD8vEx8tHDOdxr4ngjqDO1i7uI25cfdYLPVesvJZz8NrYOWdDhhPbeX1Zr4Pzh3z3/JdvB//v5Yu3o8fng++93tQ7/a3lBZtdqGtZ+Tq53Md10VEhvt6f92d4nxn2N2Eeg+ctSjWfwSetzAwfMZpKa1c4rgZtYz7gei2j/In0PbM6F/Lx3Go3x5q0THiSgfH03YXr+VgU7e9mzwLbYspXve3/vgPlPbwve/i32vheX33m7oNEZEQvKPsdY3HnxVoh9r/RUTeeudrSvvjb/5/oO3d742g/sarRg5/46rS/ov/8r/GbT84hPrzL7ygtDDAsefhg0dQL3PsS99/9y2lOTnOC1959Q2oX7t2XWlFivOxQR/nor/yK78K9eGmjhVWyNze24O6Z9wP3vse9l9EY75r6Xl1fPx7nvG+kG3sQL011PdsL8NrsxJ4SSQE53Jk5EVhB8dpP8WLdSXXc/SVT2s/FxHxwJuniMgheLO4tnMN2vY28d5/fIBzyBrMc2yMMRvhmPDk3e/pdq/reCAisn1lG+rXruO3lp/6gn7fOXxyBG1PZ0Y+YryDVeCdqamN9y4jR0N3YevdvijxeVFb78DV0/2/FfyfGoQQQgghhBBCCCGEEEIIWQv4UYMQQgghhBBCCCGEEEIIIWsBP2oQQgghhBBCCCGEEEIIIWQt4EcNQgghhBBCCCGEEEIIIYSsBfyoQQghhBBCCCGEEEIIIYSQtcC/qGG708YNBLqJIAigbRjpCuoiInlRQB3VZ29w90QaXIl9Y6j7HTpLaBuFeDpcY5pm04XSfG8Gbbs9PHbxtFTkGTQtCqwHAW4brUMct3A3PNCRH6C7oJJ9XdfQ9jI4Dlp1Ed/H39+s/qF2rLZXIYgiqJ+dnisty7Cfx13cRlFgn8bTjOe+HeO2HVfPRZkmxu/htsvM6p8eZ9NU0DZf4N+sOtreT3FcqeZ6rkVEXB/bz8favtPpQduw3YV6ZuzRCLhY0OoY/cNxJQBBoYe7IX/1l34O6q9++nWoHzw6UtqTQzx/q5CmKdTH47HStra2oO0sM+KgoaNIUF9yz/sgnnjGOj0+OIB6kmCffvnll5V27do1aFuWeG998MEHSrty5Qq0vXPnDtSt/Yz0psEnrqVb8biq9H622rgM6BwSEdnb24P6Zcb+cZwXy4XOGUREKhecDQm2DUI8Zrdv5Bhguxy8ewJNz9+bQ70u9B5YFHiPVwWeY8f4NzSOr33GMXyjrHD/4KFY4zPHa/CZ2GDXlTrQOZPv4ZglxpnYLEZQbzd6zQIP59p39l6E+v1HHyrtOJlC2zTHg8yWuN8VOEJbhputyo98+tNQv+nrNb97/21o23/+TagXQQz1ugKx3fIDD8cDV/SEuFYubISUy8Qax/qnaK4RT5Fs/J5r6I2RXza1btyz2gY55592Bqo1WIincWY01r/la3A/HMeaVzAnSFuRg0ffg/pguKO09mIIbR0Px8KusVajh/eUtjXch7ancxxrHqf64EHvBSIiros3XWDcyVvgPt1x+rhtFMREZLutY0IcbEPbwjXiuuEadaPfGE7DQ2h7f/4A96+H87otMM6brRvQtj/E43nlE28o7ei+zi1FRNIUr2+Y4pj66P57SmvfGkDbVXh0eA/q/91v6LOhzLH//9WvfAnqyyUe89/7B7+htIMT/Obzi//Wr0B9AHz329/6Y2jbauO7487uVah/4sXnlbbZw20M+kOoFxnI64y41jLek/oD3Lbn6dhbVHhvvfjiC1A/HeFcKgf9To03jWSO88jlUuu5cddvb+n4KyISGJf1qs6VtjD6sQpRF/s6yjHqCgewutJ9FRFplrjtz795W2m3dnCeXZ3h+cxA/yrgLyIijx4/hLpj+Omwq/P1wMFny84GXtet3qbSohqPZX6OY/3uVRynf+Xf0Hnr+Bj7+d/7p9+Cetngcy4AZ2tg3NMtUN5l3fXNV3rjPha7eh1i/4e/ZPB/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/KhBCCGEEEIIIYQQQgghhJC1gB81CCGEEEIIIYQQQgghhBCyFly4UHing4sNhaEu6GEVObUKAqEioiK4eKfVtjS4sE2no4vVNBkuwLm9pQvBiIi0e7gAWRcUIT89xYV/pnPc7zDWxbbyDH9rynJcCCYyCrBbRZwQVqHCxii8UxR6vh2jguFlivtZ/UAFpkREAqPwHCpiYxe2+eHJK1z8+/RIFwpyjEKTp+fYZwYtXDDUB8XzYrAPRUTyEu+tZKkL2eU5Hou151zHKNIOCtf7IfZFBxXIFZE80/0rE2MsoFilCN5bH7Wj9//k6DHun+G6boCLYIV9UGRthovOOUYxpCbQ674wiqNtYReRZ7+EC4Wnpe73H3ztu7iRFbAKOC9AoWTrbLFigeWnBShQVbpW0WIL/SeRsbdqI67NjeJvb731ltKsouJvvKELOYqIHB8fKw3NqYg9f5fRrdh9mTPbats3CrAXBV5f9JvWWC7TDwurjVWYGkVV+8Oe0vIc+0ZvE++XzhIX43z0ti4KPnkf+4xXYV9PM92XqjEKEhq7y5p5F/2JVXC3NnLIAhfsg/0wChaXRt7VgP7FAc7FyiVem/wcn5U+OBajllGU2TiL+n2dn54sJ9B2OdfnqohI14hxfql/NIzw2FcljvBhdnBPn099B693p8HjXng4/yhc/ZuVa8R7Iwf1wTXKs/bGUygUXhtnq5Vlo5atPWDV8m6M+xVySs/Kmaz6kca9oQK6deZawLGb/5bvcoXMYVc+hkLhDx5+CPXNVBco3sxwkdPhcAPqvS4+MxZLHe/nJ7j4aZLhmOyBewC6N4rYsU2MO1MQ6Tw2rHFObqSAErT0vnUdHNviGLfdtRJwUJh9ujiAprPsDDexgc+MBNi3h7gbURfPXwiKs+7t7OHfW+AJ3L+GfeqZ69qnlgtcTHsV/utf+8dQX0z03P/MF16DtmWB86Bf+2e/D/XTiT53fvJn/gq0fcEodP31r/620ubgPi4isn/tJtS/8PkvQ/2Ln/+c0oYD/H5VGkWis1T3xXzDsfJs4zRqgG7l+y1jz91+ThelFhEJwbuI1Y9z4z49Pj9X2tIoNl75OFYcGoXFxyOdh5fNxc/9i5JMwRuEiOQgplcZjsdVivUvv/wS1H/y8y8qzZ2OoW1nCxfL3rypz5F3734f2t7cx+fWsI19ptPT+d/untHGDt4vTQh8HbzViIgYz73iVDimb2zqWPqrv/zT0PaDx9h3f++7j6AegTwq9vDdu3KxT6O7uvlWm+BYVhtvBi/s7SrtUy/g2HkR+D81CCGEEEIIIYQQQgghhBCyFvCjBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkL+FGDEEIIIYQQQgghhBBCCCFrAS6BDmh3YqgXuf4uUjchtK1rUD3+B+hFUSgtz7UmIhK1cP+8SPcvCLrQttvC1dkPD3FVeb+eKa3VwlN6fHqCf7MZKs318PzlRQb1srTmW4+nMirQF2UO9TTDbfuepzTHwd/ImgbK0jR63R3Hgbaui9v2fTzfSA+CAHdkBdLFAupd4I+nJxNoO59jn97odKDugjmaTrUvioiMZ0uoF6XWmgbPvRUlIrC3RERacUtpcW8D2roN9kfkp1WG/T+d4XnNE7w2VaLbSeoptJUc9y/sDaAebOwqzQlwbPIDPU8iIk2kN8x8ivs3PrsP9a0r21Dfe+51pT17+ya0XYUwvPgZMDXGZnijNEZAKV39N0ojPng12AAiAlfb7AiW89yIpWmqtPPzc2jb7eIzygNxNzP2BTo/Rey1sWIpwvpNK34jrHh8mTzBGqN1XsSxkceAdi4zHxfFC/T6iYjkqfbHXr8Nbf0M9+v0/RHUjz44VlqrjWNPZ4DPnPyxjqXVDM+95+IxukZ+gHzGMTadA3IGEREH7eca96M2cp3c8Du4W6II2voVnj8vxfE4CLU/hv4c98/I3ZZporQMxBoRkaoy4mGIfU1yPa+O4Hlala1tPEfzE+2rGw7ug1fheFqV2PeiUK9XGeC1rT3jN4Hf+Jb/GuERxXURkbrWh0xlzL+VvqG83IrTjoMPtdrBfoPacQ33cKyLgEED2ja2vzTGYYxyBSvntA76RvDaVLWe16Z5+v9O8HyE89g013t8Ocf5fjrXMUJEZGMTn+Ptjs4/8gSfL56D5ydELlPjM8PKGhzjLHFALEyzMbQ1rtOySHRfqgl2sG4b5w2lh+PmUarv+3fHB9A2cbE/NhnWy6Vesy4O99Lbwv7ogmCRZriRK9eegXqri/O348fvKc2xkuUVGD3G94af//HPKu3gCZ77/+73vgP1oLcH9V/8pR+7YO9EPrx7D+rHx3ofXb16A9pubV+B+vPPPwf1br+ntMB4G3PQI4DgHKEw3t3yBV6DLMKbrrezA9rG/cgz/JutFs5db968rrTIx/Ej3cfzOlvq+Dme4fj74AD71P1796CeVXoP1G38jrAK3hjH9BrFOyMvakc4j/33/51fhvqN9qnS7j++h23vvAH1ZxodT+4Zbbh+H+qdbfzOtLen9Z1NvC8Gu7iNzr72L+tN7+DhE9xGeQb1bqz9tBvjmPmzP/U5qJ872JcePwJ+apzD0uD9gvLTVozz5MC4N1f4dUU6m+CtY6Dj2EXh/9QghBBCCCGEEEIIIYQQQshawI8ahBBCCCGEEEIIIYQQQghZC/hRgxBCCCGEEEIIIYQQQgghawE/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwFuEw5IIpCqHvexb+LOI4D9bquoZ5l6YV/r3FwNfeq1r8Ze3gsTY4r0+fpFOqHJ9o+KWNoO50ujbZ1vze393H/nAbqdarnSQTPa1HgeQqyAOqo6r2lOw62bWqj38a6IyzfsfoXhnqNkbYqEe6WFJHeWstFAm3rCrfRNPgPJhPtS8en2EeTAs8xWhLHw+Gg021B3QsjqMedjtJCD/vAfDSCelHqfne6XWjbGwygvjzDvj5PMqXVOe5fOde2IiL+Eq9lvL2ntPZgE9oupudQny2fKO3hwUNomye4jSKbQd0L20pr3B1o+3GA9mtqxK+mwWtixQKJ9NhOArzn2y1tKyJSLhZKqyorTl0uJvWA//b7fWhrxWk0J9b8zaY4Jmzv4PUuy1JpVoyeTCZQ74C9LyKSZXofWfNksQBrY419YMQE1A8RPE7Xffr/5iMA54KIiF/p34orHO/ufvUE6vffPYL61rWh1l7oQdtuF+vTDOQ6Y5zTuD5eV9fF+wXvZ2PvG2eiK/o3mwrvoTLDbfsgfoiIoG4XIDcVEclSHbtFRFrdLah3PL3GQRvPX1bkUK/B/PkubmPYw2dROsX7YhfErFqMhGVFOh2cO289/7zSTr77+9B2WOCcJKixL4jo+Wi1t6Fl5eH590ACF/k4Z/J9Y/8bsbAAMbm08mnjvBTRsc04QaWx7mg+vh84oCXHOreNfnugfyIivqPn1YohnpG7Ivum1nMqIpJm2EdyI4cuXf2b9cfw7wTLAs/9bKL9MU9xPj2b4nx1PMN54pU9vQd6fXy21xVe13Suf9MxzwA8b55jrKujxx628B6qKrze1VLHcN/H/ah93Mbh+THU7y71WTyvcfxwjd2YneM4m4Mc5lRwnjus8PxFgY6Ti0TnVyIip/hZRKbnY6g/B/KN6BLvRBfl07evQ/3s8FBpv/nHd6HtzZfehPr167htFAmOj3E+Npvj+axAHLx+Ff/elWtGPxrsj0miczLz7cQ4i6pCtz2Z6DkVEcmOcM7Z7uH8u7Opz9skMe4vYCwiIqMxdshOrH260zLOYevuAfw0LY19aOyX5BTPibun17L+GN6kXtowcvi2Xu8JiNEiIp//8c9D/bOfvgP1J7/7ntLQeoiIFCW+l97c12fOzf0htN3cwmfRlWfw2+mVLZ3bd3x8bhVGzHz3rr7z/j//zq9D2zMjaP6Nf/vnob53Ree+0yP85hOH2Hf//f/5fwD1X/8nv6G0t7/1dWgrVpoMcv4wwuvbbmF9sTQaH2h/nRr3lIvA/6lBCCGEEEIIIYQQQgghhJC1gB81CCGEEEIIIYQQQgghhBCyFvCjBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkLcJl3gOfhiutBEFzY1nEcqBcFroqO7XEbi6Wuzi4iMp0kSosH0FSy+TnUz86OoT5P9TehosFtOy6ek/l8qrQw1tXgRUR6faw3DR57nmdKK8sS2lprZuG6euyO4DaaBk8K0uu6vlQblk+FYai0OI6h7SqEYB5ERLJSr0kFNBGRusJjmMyXUJ9NtZ6keF0dMA8iInGo18oPDNsWnrd2twN10LQkoxNoOx7Pob5IUqVFZ2fQ9vr+HtTjIIJ6ttTxpi5yaOt7eG2cBV4bL3xXaYsUr3v/2g3cdlvv85ERg7I5nte6GGL74k+UFux9AtquQprq9RMRyXM9zz44Q0TsWGDFqpajY0Rh7LltHx99CxiTjKAuuH9RhP1uuLGh+7G9DW2rCvd7NBopDcU6EZHDoyOoz+Z4z82BniT6/BQROT09hboVY8/A3rVieqvVgjo6u6w2hsMh1C37nZ2dC2mr0gN7W0REltqnD94dQ9PRY6x3t7Af7L3QV1qwjf0/qXTOICLi9vS8+V28b/0Ct23lKY6n227AXv5Ta6jWtY4rtTEWP8T+5QnWa5BHLVLcthfg2OS5uN9lsVDa5Aznw9kMn7cbV68obbBh5Xl4bdoVbrs10L4T9S58bbgU3Z6RZ8R6fN3qJWh79923oN7buQb1ex/eV1q8j+du62ob6gLcuqmxfwQ+biPy8ZnRCrTfeK6xv4wzowR60+CzS4x82glxnovc2nfwHggD3O/YuHq0QFzwjHzM93EsimMdEx0Pj326wDnLyQjneqOp3qdV8/T/naDnYt9wHX2RzRJ8h53NcJ64XODzfTadKK3bxzFCaux3vnfxtwHXiI9WvPJB/obupD8IdMLUPu5HNsZjPMt1/BYRmXt6/3sO7l/s4DHmgu8kqJUk1e8IIiKPZg+h7oN9fm7c0RLDd565gvfcy8/r+4RxLK6E5+Pz4rff+lBp155/Fdp+6pOvQ/1shO+aDw8OlJZlONYfHR1C/ckT3cZrr7wGbXd3dqE+GY+hHoK71P7+VWi71ddnu4jI5PCJ0o6++gfQ1jnAY2zFOFY4va7SZhHOux49eQz1NMfxuB3qu9TSuL9Yd8sSvEU2Fc4HusY9xTXeUQTZhzi2r4KRlksX3Euf3cHr9Ku/+BWoOzW+O0Yd4HfXnoe2735wD+qBo33pqpFzPf8MvpsZSyK+o/1gMsZ+9OQM35vPCn3efvVb2P8T4z3pr0ebUL9yS+/zxZmOEyIi+QKf8U46g/re7r7Szq89A22LDN890Du/9fZaGvlAnuF9NBjqsX/mSz8GbS8C/6cGIYQQQgghhBBCCCGEEELWAn7UIIQQQgghhBBCCCGEEELIWsCPGoQQQgghhBBCCCGEEEIIWQv4UYMQQgghhBBCCCGEEEIIIWvBhSv+ocJcIrjwl2VrFRaxCnyhop5WAVXPKBSXJboYTJHiIi6LyRj3AxS3FRFBtQBHU1ysJTSKU6K6l+fnuDBX3MIFiAKjAF8DKhga9ZHMwkmXwSoUbnGZQuGXaUMEFyS2ihevglVvscgvPvdWMePJDBcymkyBPxpF6DY6uBBVEGqf8X3sXy2jIHgrwvYemJQkw2N0jd+czPU+Ko2CbFsDXHy3MYq5zmbaD1pGJbuog4vR+UZx5uW5LuJ0XDyAtp9+6RWoX/vU55UWbutiTyIi3//av4T6+Bj/5tm5ntdnr34K2q5CAQquieDC010fz30DSzmKGHXQ5Hqp17trFBYdpngzTsBPnhqFr1xj85cFHk9V6HasQtxjoxBgDooWTye4SKRVhPz+fV0cV0RkNtO+kSxxDMqN9bXWfQnasQosWvkDYrHAhTrbbVxgziqqvre3p7SbN29C25/92Z+9YO80W9u4wN3Be7o4XWrkHc9++lmohx18BoB6rbJM8NwnKZ7P7X3tS16NbZ053nOTY1zgzqv1fnGNgrslqsosIlWti9B5nvFvdhpcsK7IsK+X4OAOY+xfvlEIdzHGhUdrEFuqEseV8QHeF1s39fnX2sP929obQr23h2PtSaoLBvd9XGB0VXxQFFtEJG+0A+/exHtgZuTwD7+Piy7+1m//sdJ6z96Ctr/0qz8N9TDSfjaZjaBtNsd+0GrhPKPX02vb6+G1jVvYP1xXxzyrYLNvnJfWVgrAH7jWuW38ZtuIyWWuz8azMzx/ozE+A2/e1AXibz13HdqmJc5zey2ccdT5MWgDx6dVWII7rIjIoKtjclVjW9/HZ0lmFAYdjcZKs85a6xJUlzjOIqy3Aet+F4B8vaoue4fVbVupR+VgH02MnwQhS1xjEyXGNPnGu0gI3jrGJd4XUYTjSivSbWQJXt9Pv4GLab/0Eo6TZagL6rqxlbX/8Hz1A73/RES6V3TeNujhvf3kMb4nPTrEBYBTcK9fLvCbz5PH96DeNOBtwIiZ25tbUK+NOHP/3l2lWTlvfwMXLfbAPp++9wG0rWrj/eUJfsM6/Rf/g9IOjft7XuKY9cKrn4R6FGofi0BxbBH7noLe4/qguLmIyO4uLuLe270C9WWoz22zqPgKvHUf+y46l3/kzZeg7Y+8gfXk8PtQ723qNXRAcWkRkb1rQ9w/kGdfC29A2zYoTC4iMj4fQ30x0vYHxt73Yxwzrz2r1/tHXsPvMu+/j4vct40zsZtrP7i9/xy0TZaPoP6ND74L9ZavY98Ld16FtmWGz4AWqMBuvcXnBT4UncqIQ30d47oDHJsuAv+nBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkL+FGDEEIIIYQQQgghhBBCCCFrAT9qEEIIIYQQQgghhBBCCCFkLfAvaui6nqGv/l3EasPz9G+atj7WHadRWpnn0LbOcTV3J8X2fUf377zEtufzpdE/3e+ymkHbIAqgfuXKlQu3LXo6nhpNg6veiziGve4M0n7wb2L7utZ9qSq8vquQJni9y6zQv1/i+WkqPIbZOMP6XP/mzt4A2rbjEOp5ofsXdyLcRojbCMH+FBFZzhOlVcY6dVsx1Fuh9vVZhuc6M/TlBO+5dku3PdzsYNs+1rtdPN9hu6+0cvsmtB1s7UA9ivWc3PmRH4O2V5/7BNTf/ebvQ/3JwSOlPfvK69B2Faw47fv6yPFBHBURycAeFhHxPRxPnEa387YR69+MsK+/4ul+/46xb61ol2Qp1EPg02Lsi6PDQ6jv7O4qbTrF58V8OoF6ZXR8OpkqrdtpQ9v+APv/eDyGeq/XU9pisYC2sxkeTwjikBX/rTMxTfHaPHjwQGkfx3lxfHYG9aCn9/ytT+L4UHXwmNsRjtNVos+RwNgXmy0dv0REUq/Uv3cNx0ZXm4qIiINdRuaPdJyu59i2qnDjJdAdB+dLjTF2H+1PEem2dKyojFwnm+s9JCLSVPg3Wy29v5wAx8NigdsYfV/7lH+q95uIiJd1oV7fwHuxDvVZPjfWd1WCRuckIiJlA3LkCDvT7dc/B/VH7/1DqD/58K7S3r2H4+Yrrz4H9b/6c59V2pUNPM+LxTnUPeO87HX1ednvYT+NY6yju5t1bQt87HsBPnLFBXeMCuSWIiJS43gauTieffhQ5ypvv/0OtM1zHNd9V//mYIhzzsrIQ9rGftzd0Hv39Bzv0VVIEhxT4uhUaY3gzbm9tQf1uRGvZjO9B8oSr2tlJBRhoM8j6x3BwnGw41XgauR6+PyzaIA/OiX20aI0ztwOPi+rRB9gaWXEtxpvxqBl3N0S3ccsN9qOcNuJ6LguYuQE/S2od0OcnxTOUGlVqLWViVpQRnfKmZELPwA5r4jIIsN3b/R+8vj+PWh6dnoCdT8Ad9s5znnF8P/tbTz3HrhfWTmydW+YTbQ+S/F85DG+H0yMWPr+N/9IadEOjk2v/ORPQx3dJUREoliPvd3G/TPvGGBteiA3FxEpjHebsIdjQgjOyiLEOfQqzB28L9JUnw37r30e2sZdnL8cnOB7aZDrtsdT/P5y8zZ+Dwk8fY6gdzQRkcPHR1A/PhxDfTwGZ5SH8yVf8Blw8p2vKe3OFrZ9cXgD6s6pzmlERP7l//sbSnvm9j60fek63i9ejPfoO2f6HEldvIfqAudRRaHPhsS4S7/2Kt4v82ewP2zu6PPl7BCv70Xg/9QghBBCCCGEEEIIIYQQQshawI8ahBBCCCGEEEIIIYQQQghZC/hRgxBCCCGEEEIIIYQQQgghawE/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/sVNQfV4EXHQd5EGt1DVuFJ8Y7SNGnJdB1q61ucZR9tnBe6g6wRQDyrc72HgKa3ZGULbx1NcmT4HXamNeZrNplDvdftQ7/e1Xtd4/kQM3TEWE2BaNvhPaqA3hq3Vtmlfa5+q64uP5aKMJguop1mptBxoIiJFied+Mcd+4AG/i0KtiYhkCfa7KNS+HoN2RURcY/bLLId6ulgqrddpQ1vP8Lvrmx2lPUrx75VoE4lImhVQ3726pfvR1b8nIpKUOLDUHh7Pjes3lDa4ojUREdfD4TdP9Px5PTzG/v5NqL+xvQ/119K5FgMc91bhzp07UA/CUGs+noej0zOoT+5/CPXvJ3psYyOm/77huz8T6f7dcfA+fNvw3brA9oVovWP43dHxCdS3trTvbm1s4jbOx1B/9aUXoX56dqq0GdjLIiKnoxHUl0ts74BzOM/xGlg6Os/KEsfUhw8fQj3LcDxE/Y7jGNqughdGUPdbWi+SFDfSYJ+uajxv80yvq4NDvYQh9scFWJPKm0FbL8b9awn+Ud9tKS05NXzjEOeKNTjvLawcshPrvS8i0or0eEorlSjxmsXWuge6L1Y8LMGZLSIymeq5CjI8xskjHFe8Hu5fuKfXrKoSaLsqbQevYeXq+cgqPD6/i8/lz3zlc1D/3juPlPaP/vkBtP0Hf+e/h/onX9Zn7Wc/8xq0FcH7KzJyr6bSY3d8Y/Mad6AG5r2Xy4VLfKRJA5ahQqKIOMZ5uVzgPTNN9I9Gwz1ou9HGe6YAvnP3AV7fuNWFehj3oO6Dtn0Hny+r0B3gvVk7E6XlGV7XxQLHjrLEey6OwT4y8qCiwnl2b2NXtxvhM/X09Bjqu3s4jz07O1faoD+Etta7w2yq56+s8LlTB3gNlsZbQjbXuWjt4A0ad4ZQz422C3APimMcV8ocr2+e6T3nCB777/7m70L95O33oP78Fz6ltBuv4/i7CqG1Jks9tnmCzyxLr4y88hzkvScn2HcLI49F97579+5B26PDI6jfuvWM0bY+GyYz7eciIqMznAsUbb33F9YdbYz7V4c6pxMRiTZ0/P7UZ38U2u4YYywEr00X5dAZvo+4xptjv6fPgMo4txwf54txB7/HOeCdovaxD69CCO7YIiL9rWtK+7Gf+avQtgXuwSIiubFf8hStiZGn+PgsSgr9lvb40SG0fXBX520iIqXxxuaCc6fVxffm83N8r5mc67azCX6TbRlvKkf33of6cqn3Yp7gvfWSvAL1Z/dvQ/2b7+m78IlxT0HvgiIiHogrg55+ixAR2QDnvohIauzFutJ7MR3r8/2i8H9qEEIIIYQQQgghhBBCCCFkLeBHDUIIIYQQQgghhBBCCCGErAX8qEEIIYQQQgghhBBCCCGEkLWAHzUIIYQQQgghhBBCCCGEELIWXLhQ+GSsi02KiPig0E0U4mJ9QWQUjjEKBDYNKPpi2LpmMWpd+GSS4W85W50B1IcbWK8XukBaq8Btb/Rx4aTC0/2D4xaRyTko8isiZ6d4bdqgwB0qJPXRb+L5QwVeP9JRkW+jcC5UcaFwq5a3WRDcWnfQkGG6EsdjXPwmA4XGcqPa4mKJZyg1Cg53QCGo3CiiXXnYHzuR3rdOidsoQCEfEZHCKDLo4OqRRhu4yGAB5qo29sXZDBevittGEa6OLgZ2nOHCWE/OcCEob4z7nfd0IbSXtvHYtwOjUPhM+1TQxXvf8fCec42ihFGs41BlrM0qtNu4cOHWpi7O1evj+HrjxnWov4eKnYvIH72rC+L5LaO4boN99y1Q9PJNo0jeqML78xFeEpmdjZVmxePpWNuKiDx6/FhpN27iYvG3Zrjg2U/c0gXjREQmE/2b3/zWd6BtaPguKigmIlIUel6RJmKfOQkoUme1YRUbt84L19X7xbJdhTTF/Y1bYD4Do69G3C0aPOZFovdLL8RFcZcLXaxPRCQtdEwKQys3wGNsImw/uKrzlLoaQ9visVU8Xfud5+K93+3iwr+m76ba76xC4SEoAioi4ho+PQa5W2QUaWx8HNMdkINnKT63igL7iDzExS1bvj5zSw/7yKocPXkC9bMjXURxe/8KtO0+ewPqz736MtT/+r/740p7cPo/Qts//A7Os//ef/lPlHZ9Hxe0fuEO7ndVG/EKFNusjX+LVhrFbetaxwsrP8iteGoULUb2pZHTZRkuop0tcP6GjujtXTx/8wU+6x4f6cK+1WPcj9Aobhu38N7o9Da06OK9uwq5lWe7Ov9ojHVdJDhuvvDcs1A/H+vix9MpjilNjX/TB7EwMgqo7u/g4qKBkd9ubOoY3kdnqIg8eoQLIi+X2u86Lbx+N6/tQP3eo3tQ72zomJws8P50jELrVm4YhlqvKry+rlHkGJ3dxjVPnpziYr1ljvft+OtaO1/iscsv4kLFF8ELcNF5P9BrWBv3Y+SjIiLJHMeT48MDpWWg6LqISATu2CIirZaOM1bOe3KKfff6dZzDRyEqlo3X6Y/+8Ddx/yJQLPvGNrTNHuJcoN/HMfPzn/mK0jZv4DO7NO5RjnG+VGAdWjHez60Y+w56c8mM+GsVvI4H4FwQkWqs16FyLvwEe2Fu3bwF9V/6n/5Npf3cz/4YtHUPvgp13zjfvKH2md1tPA+eh3OJs0PtSx+8j4tln53hN4BOD5/hnbbu996tfWjbGuD3CDfQe+t4gX1jPNV5h4hIq43PyhdfuKM09IYoIvL+e3hOBk+w/exDHb/fOsJ7SALjzAHn9v4+nr9O17hbGkXmUS4aRPgbwkXg/9QghBBCCCGEEEIIIYQQQshawI8ahBBCCCGEEEIIIYQQQghZC/hRgxBCCCGEEEIIIYQQQgghawE/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/kUNe/0Y6qen50o7OHgIbQfDIdStaumIssRV2+sKV35vKl2dfpk30Dbw8Bi9Aa7y7pa6wn2Z4QrvaZJivdH27S6u/N7uYP18tID6fD5XWn+wAW1F8JxI40C51tMqtdFGYzUN/gBpP0iv64vrVhurcDSaQL0Cv18WYNJEJMuw75alMbZK61lWQdtOG29xB81bhduoDB/IlxnUF2B/JTXeFycJ/s3vHup5/f4jvd9ERD53pQP1T93qQf0s1evw4Tnen2cJXrPY8Lv0QPc7H06h7Y/u5lCvCm3vjE6gbdeDslQ1nldxAt2/Oe5fd/s6buMiOHh+ykL7epJg3wiNeJLmeN4aR3+jbwT7boBl+QCs64axL15x8eQnPu73GMTjh/fvQ9vZEsf05qE+W78M5lRE5EeN/p2+dxfqIKxI4OJ/9+C4eAI9D/+m42j7Gh0iIuIav3mZ8yKKoku1nWU6lg2NfGUVQiPHqDKd14TG5m55eg+LiMxmOIa1WkPddh1C2yDC50Umuu1WF8fdJJ1BPTVyNwn0b7o+HmPLx/32Az2vftjCPxfiPGphxMGi0DG9MPbcoD+EelLiGHL45InSOjEeYy24jeGejtPmmT3DsTae4N9EPtVEODatytLIjw4OD5W2s78DbTstPI5lg30v2tT2n/zcDWh7tsT+9NXf/ZbS/pu//xvQ9n/2H/4q1MW3zmsdAxoj90iSJdQXC71eOYg3IiKLJW7DcXFcyFN9Fifg90RE/AivjWec8zXwh3GGfWQB4reIiAvykFaEY0tjxBzPwf0ugO77+NxZica4H4B7w8Ymvt8FHr5jX7uKfX0y1bm2I/g88ly8fpOxbqNI8Fxe2dmG+ukZznsbT/tjbbxmTIzcuQB/ITbyaakM/2pwLtrr9LVtjduYnOM7ZK+H16zd1nHo7AzfjdptnG/EoR57auyh0NgvVYTPmJPxsdJmf/z70HYVFkuc79QCxoHDhgRGLjUd4zWZTbVu3THQXUdEpAveu27dvGXY4hzLymPLSvtjbOTCezt7UD8+1jmJF+I2Pv8zPw/1zcEm1F3w76hzFy9OZcyfJ1hvd/S+iGLs/4nx5uKCY7iqjfhr3IES405SgjPUN+Z1Ff4nf/M/hPov/jt/XWmDEOe8p2APi9h5edzTby2hEXuW4zOoP75/oLQReF8WEYnb+G0nBrFRBOf805lxx4aqSFHrvVWG2Ac6uwPcjx5e7wbcgRrj/xzMU7xmhw/fhfrZkX53+PBDPK+ZMXgX3N/v3n0f2sbGniuNO5AP3gxCI1e8CPyfGoQQQgghhBBCCCGEEEIIWQv4UYMQQgghhBBCCCGEEEIIIWsBP2oQQgghhBBCCCGEEEIIIWQt4EcNQgghhBBCCCGEEEIIIYSsBfyoQQghhBBCCCGEEEIIIYSQtUCXXDfY3MLV3Ls9XVV+NBpB2yDAleldN4B6URRKSxJdyV1EJEsTqC+X2n6ZZtB2UerfExHp+R3cdjNT2jzD/SsrXPm9rGqlpUkObVutLtT7fV2ZXkRkuZwqrdPV6yUi4vlGtfkGt900F9NEROoa/0Gthy5NjX+vMtqwf/PitquwWGJfSnP9Y1kGOiUiRYE7VljzVmm9RgMWEdcYdFNrfyyNfowm2s9FRB4fY1+fgDW8Pyuh7ffn2NefLPRe3BXcv/2eB/UkwWvzaLFU2tuHuB+p8ZvDbRzL0NZN3v4A2l4fRFB/6ZWXlVYUC2hbZTgm1Ea/nUbP6+zhO9B296XPQv1CGHutBH63XOKxFQ7+5u63Yqy7+kcbY180LvYZtLe+WWHfeDPA59YXQty/B329VnmBz4X9Fo7Tr4/PlXb7AMfM394aQv3xwwdQ39y7prTBBm5judR7SETEDfCaofjkOLjfln4ZW9fF/bB039epUK/Xu3A/LopvDK1E82O0EUY4bWtVOCYtl7qlIMA+2jJ8d9vROsovREQ8Y9/GMW7bcXX/KhefF02F2w5Cnb+gdkVEiiKFep7jfY50yzc6bTzG85MjqJcgXxwvcT8cD8eb2tfrvnv1JrSdLXCszRd4TiKwDHWIY+eqRMNtqO/dfl5p4yXO97/53feg/vgAx7z3v/k7Sjs+xnM0vIrvAR/e1/vgH/3ab+A2dnAbn/nip6Du+Nrfe2185h88egz1P/qjP1LafI5zNys+Xrt+HepXd/aUtjkcQtsajEVEZHdnB+pZrs/G83vH0DaO8f00AnGh1cJ3nSjCe1c8HG+nIMdvjNiyCl3jajZJtZ9udq9A2yDE83N4eAL1qtDnURThnOT8/BDqKYhtRQvnvHGEY3UPvC+IiDieXqt2C59/w/4Q6n6sY/irL9+BtgdPHkK9yPCbQRTocc5r422gsPYi9sci1/azqbGORk7Q7+ux9425Hm70od7rY/u9KzomDPrY/1YhNd5xUAzzrFygxGftZIpzG/SME0VGPDHuKVev6bP51Vdeg7Y3btyAequLz4DZWL+9OQ6+Az3/3ItQf+bWc0o7B+2KiPgBjo2ukeuhe+kixXcJqXAs3dnE/ohivfXoY51zDci6S+Mue57gHGSWYb+MYnD2G/O0Cv/mr/7bUN9qa18fPfgWtJ3PcDzZu7YP9e1dfe7Mx7iN8fkY6o8fP1Jamhm52BbOFUPjXnN6qP333bfv4jYiHDODWMe7wVUd60RENob4nHvuBrafnen+PXz4IbQtS7zngj7+TW+h1308H0PbxHijdME9+/xcv0X8QBz81oGv8Bd/A/jX4f/UIIQQQgghhBBCCCGEEELIWsCPGoQQQgghhBBCCCGEEEIIWQv4UYMQQgghhBBCCCGEEEIIIWsBP2oQQgghhBBCCCGEEEIIIWQt4EcNQgghhBBCCCGEEEIIIYSsBbiMOqCpcTXyXrejtOGwC21brT7U6wZ/Wzk7O1Wa4+Dq7P3eAOpp/kBp0/kc2s5TXSVeRMQoNi9ha0NphYvbruol1NuRrlifV3iuXceD+u7uLtQfHzxW2vlYz6mIyM7OPtSbBsoiovti2dZ1CfUKjNNqo2nwnNQ1/gvYHrexClmG/XG51PqswH1NsNtJYHxyrCrwm8Y8uMaY61K3cX62gLZv35tA/fvTDOpnIFY8mGPb0wwP/lYnVtovvLQFba90A6h/7xTvufcXeo8+MsYyzbB+rcbr7pd6Hc7PR7gfQ7yfX3n5RaXVjuG7RkzwAjwn+XyqtNnjD3DbK5AZ85bnWm/HbdyIj8e2sX8N6jeWuu2j8zHuR5pCvXH0+qXGt//voH0oIp9vKqh/ajjU/dvSmojI808Oof6Mqw+jv3t2BG1P+vpsFhG5lRRQby31ftnY0GeciEhR4DY8D69ZAwK7Y/h0nhsBERCB8/MH4bp4LTsdPVdxrGPQqnRauL8NOE9PJk+gbWrkL56L+xv4en+5Pu5HLvgciTotpVUl3kPdzg7UJwmOx1mufcntYt+o+nhvpZXuS+zgtS5zK2cwzlDgM76Pk8LUiHtJiXMgB8Rp1zPyP8N3/UD3xTdiZ2n0Y3Y+g3o71zl7uGHE6xU5wyFF8u5QaTNwjomIlBn2saMUn4eL6AWlFf0zaBsP8L7bua3n6OTgHNr+g//2N6A+LnDu9dyLzyvt5Rd0fiAiEnV7UG/8UGlvff99aOsbecOiwDH5k6+8qrRP3MH9+/DJI6gvjX2XOrov/hDHltrDsa8CefGsxjFkmmK9FnzOo7YbI36uwr/1y78A9W9/61tKe+31T0Jbz9+G+ukI76P9a/rcL3McOz7xCl7vVkf7nQ/yKxERx8iZGiPPdlzddmmcL0WG99ZkrvXJGOc78ynO4aXBQassEqVFIR57HGE99PF8b23pe9Dt23vQttvBbzFb29ofdnZwG21w9ouI1ILHHrf0Xuz18NvPKljnNaI29vxohGP9fI7PwyDQfucaOa8Ybxb7+1eVdvPGDWiL8lKrHyIi/cFQ2xq5gDV/HshtdvauQNujI3xPGZ3ptycRkSDQeWea4n27tYF9ZjjEb30eGGeBt5A0DY4rZaX9BGkiItOF3uMiImLknWFL500V2Cur0h/g3N6d3FPa/OQ+tK2MN7vYyDHyUseCZYLnZzHH+uamjkkFnnpJUhzTj05wnJ4t9f0APNV8ZGvkkD2QYwx38JvU7nWsV8b59+RUv7GlRj48W+B7V13hv5DUWq/th1bc9iXu73ZcNhbzKedM/J8ahBBCCCGEEEIIIYQQQghZC/hRgxBCCCGEEEIIIYQQQgghawE/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwFFy4UvlziQnFhqAsWGbW8ZW4UuIxbuBhSt6uLXLWMwjqO4OI4nY4uKnRrhovJ3H9wD+pPDnDBzhIUYd7Z00WgRETiGBd2qkERosLFhbmsIo9VhfX9K7qA+GiMC8NNJrgYdL+7CXX8Pcwq+GIV6AaF9sxi40YLhm7UzX7qlKDgtohInul1HRuFCBc51ndi/M2xAr9p1B8S19Bz0JdHj8fQ9uAMF3bKjW+iKeifaxQx2jaKov7EdR0TXtrHRarOjYJdp0YR9/sjbX9iVGuvDcc7PDmGuj/V+ytJcGGncYLXHRWCb4ziyZWhh5ERQ1Idg4tzHN9WwZq3GozNtDWKUbvG2G49p4u+bhgF/85OT6F+CnSrqPjSKAT4jlEorgDFVmdGoT1cslHk10EbXzX6d2uBC54FLu53mup9sQGKm4uIzGZ4Xq1ixqiAuFVs2SpCXoGz0mrDKlhu2aPiZpctQn4R8gzHKt/X89Y2fr8/xGfy+Qivd1PpscU9XOzZ8Y0DA0xnf4ALOU7GOL+aL7HPhLGO9RugUK2IiGcUMh7f13G3OsWxMRI8dt/wGZRfnRjxfwCKHYqI7N24DfUAFI9cznCOtreF192L9blo5YpWflWkeK7SmfZXv2cURl2RhwtcZD0H/huEuFiolYO6e9hX9zq3lNbLx7jlAhc/3bnxnNLmEzwWdAaIiMw8nNs8mevxlA9OoG0c4b2x9bwu5v2ig/dA3MIxJzMKc37vQBfaHWffh7ZHszHUl0YO70VDpfltvAeqGp9pLmi7MfLW2kiiLb1El49LFC++KDduaR8VEfFDvd4odxQRWSQ49iYp1hvR/psauWaaYV8/m+jzKDfOADEK0/aMQskPH+m9uL+Jz4yrV3HR1gmIN0WGY+/NG/ouLSJy5+WbUG+D83VvF58N4xE+L61CrDs7O0rb2sJjtwpkNyB3XcxxHnlmnHUS4P51S71m1nm0Co51uQUPUNPxOTQ9eoLvPlaxW9e7+L8Djlv4nhKCIt9haOWl1h0R656nY8JgA/udY9wDarAXrbAWgBgkIvLkABegRveGfhfP09Ur+C2t1dLvgiJ4TqxzoTTiZAHGnhm+O82x7oBi6CIiXqzfLksfz98qRDU+q+sp8HXj7h31cD6SgYLgIiKB6LElS3wf8VycP25u6rM9zY2zZYzHaJ7trt5zjdGPzhDH0rCvc0i/jeepdvB77/fefRfqjz44UFqV4bkufexfpbGWx2djpTXG2eIac4LOEevdxqI29qJ1zv2w8H9qEEIIIYQQQgghhBBCCCFkLeBHDUIIIYQQQgghhBBCCCGErAX8qEEIIYQQQgghhBBCCCGEkLWAHzUIIYQQQgghhBBCCCGEELIW8KMGIYQQQgghhBBCCCGEEELWAv+ihq6DTdOkVNoywVXvNzZwVfki122IiGxubup+GJ9hRudTqPuh/gv7+1eg7da2/j0Rkfub96D+7ne/q7T+AI+xH4VQH40nSmvauI08y6G+mOs2RETa7Uhp+3u70HY8XkC9qvDaeK6ntFpqaGtWt2+03uAmpAa2H+lG00A3TFfCGJnUdaW0stSaiEhRYV0a7OygaTGmXuoKj3qRpkorM9yP/Q723b4xo9tVoLRDtCAi0u9pWxGRT90cKi3DriiVi9vY7behXjzR8Wme473V9bSfi4hMFgXUy1TrLR/P39Lw6QasezbF+7MoD6HuB3hO8tm5biPBbX8cNMAPGsOPXGN3OcYhELdipfUHfWi7s7MD9fPRSGkHjw+g7enoDOphg/eRU2gHbuIOtP12g539HvBTz8Nnc5bpPS6C10BExPd1O91eD9reuHHD+M0M6ugMKAq8hzodPCd1rYPcbDaDtpcZo4hIBWKweW6tQG3kL9OljkntEM9Dy+9C/cH8COrDlv5R38H+VZR4TapM68OdLWh7Mn0A9SDAvznc0Hu0NPKOQrCeJ3pfZEvs/6cPccwcDnFu5IFQmhU4xxXjLHcKfEBv9HXOGRj7ubOBY1a3o+dvOp9D25FxnpVWduTrHNLaQ6tyUuA+pGBKXePsdI0ta+27INDzUUV4f8XyAtT7m/pHew7eR7WRwwSuEcPBujxK8Tx5hXFexnqfbr+M7zphaN3zcJw9Wep4Pz3DZ0BhzGsV4MVxXX2eOxW2rYzc2gNnsZVvWD5SWfaobePcWYW//Xf/DtTRbxW54XcNXtfAx/HAccF5aORjfoDbjts6793bHUDbjSHWe12cf+zva5++eW0P2k4mOBZmYL9sDnG+GIJ3BBGRDePNIIj0nPS62P9dB7c9ArmoiEhV6rNkaPTb83Hb6I7RaiXQtmXkY57xprG5qdem08F3sY+DHOS9BwePoO10MoZ6bTwslOB8t/LVwLiDhZE+U6NYxzoRO56gfFVExAv0mng+7ofnWf+mWbdh3bmiCK9rHGM9TbWPtVstaNuKsX/VtXH3Bm8djfHEWRj5JbpjZPDBRWQGbEVEHGMtHZDXNdaD5grEDd7Hs7mOJ2mKz+owxmti5VcnR8fa1jAuSjz3h8enun857t/EeA9ZJHg+x6Cdyoi7pRHXqlDvo5NT/Z4iInJ+iu9ikwOsz8H9pTTO8vYmPkekxr5+NAK5m+G7vnE/qEC+XZsvqoaTmPdp8A5svqz+2fB/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/KhBCCGEEEIIIYQQQgghhJC1gB81CCGEEEIIIYQQQgghhBCyFvCjBiGEEEIIIYQQQgghhBBC1gJcLh1QVbhauoiulr65sQMtreLny0UC9QZUaA8C/B2mKiuod7stpblGR5Yp7kcYhlB/+eWXlXZ0cABtp6C6vYhIpz1UWurhZfFaAdTjWI9RRCTP9Xgao2L9cLAB9abGfWksdwBYdexdR69lJbjhpsGtNEbrdYPH+bTxPPz7PpDbxifEwsVteC7+C06tx+YCTcTeF0Wh9V4H+3nHwW1nOV6ryVK3XUd4jC9c7UC9BdxubOyhqNuGentaQL1C89fguS6M+UuN+UZumuW43/MljjeOHylteTaCtvViDvX29jbU82SptHIyg7ar4Bgx1vP0eWHZ+r51POG592DcxG3HkZ5jEZH9/X2lDYdDaDv6k69BfTDFa5WNdf8WPt5zo/EY6mVZKs2Kr/M59o3z83OoDwYDpaH1EhHZ2tqC+mKxMHqjSdMU6q4R9xoQ03NjbxUF3vtZlkEd9dv2vx+exogbnbaOg6GxsvPpGLcR4/4OetrHyhTv+VaMY+k80fOZzvBabwyGUA99HXtEROJQ5zVjo23HwWdOZ6D73Wxg28V9I95Nsd/tX7mmtCwxfFf0/hQRyRZjqEehjkP5HM/T2J1Avaq1n1gx1cplHQ/vFwfkJlGI881VmRhJZVnrM9jaG0Y6Jr6RDpauXq/KmLu0wXsD5dSui+NmE2C9Nv59GbS2/imakac5IG66Rs5ZGPe80o+hHvb1nJQuvqdYeS6654mIlEC2cqnKyvdr7deVdWcwJ9ZoG83rx3DtKCu8N7vdrtJefe0VaLu1OYR6z8idQ3D2nZ+eQVvD1WVrR98pu90+tG0qPHF3734I9ZvX95T24z/+OWg7neJ4Px7rGJ7lOD+IYhw3LVA7EyOnm0ymUP/g/XtQ9329v95481PQdnNnCPWzM50DJsb9ynLpCPRDRMQDDhFFOH6sgpVLnZ9pPx2PcM5bV/h+Z713wTcLow0/wPPT7/VAu3gTVcbeR7FHBN8FqgLnJJ6D+wcf6gwnsMbYDfRdQkSkB/a/71tvejjHyjLsp+BqJJWD1yZHxiJwXpc5bmNuPZ9aORbYF8aRuBK+4PlxQ93fdgfvyyLBY3780HjfHJ8qbaOP33bOxjjeOYGet8DRZ5yISFHju+3xqXG3vTpUWte4p8yMdxkp9XgC13irNVKJwQ6+N29sbCotW2L/bw9x/v3Nt+5DvRI9ry0fn3NZbsQK8KBZg3uHCL6nf9QP681Mt9M4P/zdm/9TgxBCCCGEEEIIIYQQQgghawE/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/KhBCCGEEEIIIYQQQgghhJC1gB81CCGEEEIIIYQQQgghhBCyFly4xLjr4krnda2rpTsOrnK+XC6hXtUV1H1ff3Opqhp3EFRQFxHxXE9pi8Uc2o7OjqE+Hp8bP6mnL2h3se2ygHp/ECmtA+ZURKRq8NjzEs9flum202QBbT0Pu0Krg8czmyWgg7h/2Bsuy+qt1LXhOytgfRUMPP0nPR//ftFoHxUR8T3s0wH40cCYH8cYs+PoRvwQ/57j4DZ83G24n5/rdaDtjY02bgT4+nAD+6IXxlB3fOCjIrLb0fbjHI8xrfBeDPBUwb1YAV8QgUMUEZFkmSotW+B9W/q4I1WVQT3qbyitxqYfC44RpxHtNvaNpsG+XlV67vM8h7aBj+OdA+LgzqaOoyIiG9t9qP/J+Qjqj+Z6XSV5CG2t8wzNnzWnRYHPnOl0CnU0f56HN3kQBJf6zVarpbRuF+9nqw00zo0N7c8iIkmC935Z4v2M7D+O8yLy8by5YGybPexfZzOcj/S7OMaG4NwJBffDK40zpwYxLDPOFuPc2uhvQr0E29lpjLjr4lgadvUeLTawH209h30mG2F7rwvmaorHPkvx2kS+Fcv0HsgWIE6ISLuP53UBzgbfw+u7sWmMXXAO6Xl6nJ2OcWavSGmsuQPOYK/B/XWMe4pv6CJad42szgFzISLiNKBttF9EpDbytNzydwfE38rI9awcEOiVcYaK4DHWxp2uAfNagDkVESmN2GIsJTznE8NPS2vsMHfFa1MZuZ75L//AfQf6wor80i/9m1Df2dlR2tbWFrRtajw/WYqTv/sffKi0h/cfQdva8KUH9w+V1un0oG2e4bm38rdXX3tWaY3hd9dv7kP9xk29t0pjb1n5WGncvctSnyWOi3POJwf43eH4+Azq05l+v/jGN/8Y2r7w4nNQ39vTc3I+nkHb8XgCdT8YQz1J9DtPEF74qenCJMY7zvGR9rvUyAetHM/KqS+TE/a62NfbIBfOc7wPG+OSaIVvlMObxkbM9MFaWXHNOBbENe68HphX1zXuc+abj3HnBTEuLXD8yI07Rgnm+yDFazD38L1QjH0u4M3FeEJZibrA+WMQ6F/b2hhC24cTHOvnUxyT8lT/5oPRGNp6DZ6fXl/vFy/EfnQ+w2M8n+OY0O3qN584MtZPQqgePj5S2skpjpmx0fQG6MdHur7r7YLzXUQk6OA5ycAaiIi4YL/0uzoGiYgkCT7PUAiBsUZECnD2iYh4xt0N5aJWnnwR+D81CCGEEEIIIYQQQgghhBCyFvCjBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkLLly9qQ2K61osjWLUWYaLIfX6uCBmmuriTlbRYqt44fnpqdKOjnXBFxGRxiwCZRUl1N+E7rz8MrR9/FgXrxIRGZ/roq2dGC9Lp4sL2HhG0dujJ7poZWoUXpuB4mMiYtbnDkEBnxLXqTGxCsxdBqto8MdR5BVhFRQLQJH7NqrwLSJLo4ifUWtLWrEuAuq7uORUZRS+Q8V5QqOYW2QUfm2MKmFhW8cKFxSpEhHxI6y7oC9tY4/PjMKq3RYulnprQxdJOkpwbPKt4palUTwMFGVqRzh2lkaRxvmJLhx4/PA+tPVAgVwRkekRLsrb3dlT2s5nvwxtV8EqJB2B4lyoiPQPasPSUeGqMMQxsxXj3/QD7XdWAdHgjS9BPV3ijZs8eKD7Z8QP3yjQfZlC61ZsTI2CYqjgsDXXzzzzDNTv38d+ulzq4pFWoXArdlsFxBFWkXnkf5Zuzd8q9Dq4mDcq2FrmRt5R4jGkRrFJL9PrXXp4bO027l+ro3O0syXOGRoP5xhOY5x/uV7XMsOFPZsK74uwpWNstIXn6Wr/CtSrOe53OtJ92byJc9YH9/QeFxHpbeICvkUCCnEPBtA2MmJWBgrzlSWOWZZP33wOF9PtPa/HeZyOoO2qRKDwsohIA4rxekZxaTFyLKOmISzEivJ6ERHPxfGnAgVAayOnq41cyozrIBYadVXNerAN+AtW7m3Wubb+ABXLdq1ik3heGyNHLYGcgbkWESlrq9A60GojPlnni+EPaM08q0jsCrzyiRegHgQ6tzk6xPfMr37161C/f/8x1LeHG0rr93Fc2tndhnpW6PNoZBWP9bF/9Vv4PDoD7ZyNcNHWtlEUtXG0H9RmnMC6hQM3KY5vV67qnFxE5Of+2l+BelnotnOjIHK3h/Mgz9N53fbWLrTNwfksIiIuXjMP5K5WHrkKJyfY10dnupixVdT2qeR4RhORcfcIwVxk4K1LRMS35s0Ix2g87iWLnlcgpoe+8fbk4XhnFTiH547VD+sNxzgDqkafAQnIfT/6Sbxoh4n29e8ZhcJrH+eXnuEQ6BgJjJiwCrMRft8sphOluSW+MzgOPk93d/W5ICJyfqz3/OQEx+P5bAz1WvRv9oZDaBuDNyYRkU+9+RrUHzw+0L9X4LkvcSiV4wP9ljw13p5u3sSxNKzweZZP9P14aRQV39zFbTjGO4WA+4Fn7M/QyE9RrPCNIOQJvqNZOWcB8r/COogvAP+nBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkL+FGDEEIIIYQQQgghhBBCCCFrAT9qEEIIIYQQQgghhBBCCCFkLfAvalgZldWLQpeKzzJcPt73Aqh7Hq6Wfnqqq833+11oe/eD96A+GY+VNugPoO21a9eg3l20oD6bzZRWFbqKvYjIiy++APU/+fY7Sjs9H0Pb6ULPh4jIzqAD9V6gv1n1ekNou7W9AfXFYgH1oiiU9uGHB9g2NSrZ146SmqbBtoZu2v854TZ6DCIioa992vqC2AJzKSISufhvxJHetpU1bSWeex/sucDDYxFD9lwcPnzQjgf6LCLiGTGhzPScjJIJtB0lON4sczx2pDfWOhqxKWq1oY7U2FicXoTbno1OlPb4wT1o29kb4rZPt6Fegv1Sb+K9vwpBgNe129Xx27JN0/RSelnqM8pqezbXsVtExHEMZ8fGUP7Eyy9DPQGx9BycTyJ2vxGXjYHLJT6jvv71r1/Y9tVXX4W61e+zszOlJUkCbcMwhDpad2u9LutTaJxxHEPbVciXGdS7LZ1jNHUNbSvBbXiGHyxSHR+jNh6b6+LfHPb7Sgsa3A+nxG3HHs6jwq7OX5ZLvD/LGsf6tJ4qrd3GfrTMjf3i4bYbcHJ3Oz1ouxltQX3Y2cRt627L6T2c503Oj6A+X8yVtrGFf282N/bzxptQzwq9R6Mwgrar4oC7hIiI1OAcN87rykqEjNzGRXKF++EYiVADcpjM6EZZ4v1l5beho3OE0Li2VTVuo3K0/xbGWIoG9888YsBvOsZd0cqAXSvOgdw1r3GuXFc412tE65GDbeMA52NuaFyTQY4vYL1WJQ5wHENn384Wjj9f+PznoP7MM8dQ39nU7XQiHL99IxzkpY5LaYrPjE5Hny8iIu0Wvu+Lq3/U87B/pTnWuz3dtnUFShPcbxG8MdCbRmbcxSrD/10f97vX0bcM6w1lbuS5S/B2IQ3+vSjCC9w4F887rfejVRiBnFJEpCx1jHBBDBQRqY25t3QB7VTofBKxXAP+QZ7jvNS7xD1AROCdxDXeEawOVuAeVRp7y7p7WGelA+Kx/U+rjTPeWJoCnK2NEY+zEs/33ane56MK9yMyfMpFYxQRV3T/avAGtirZfAz1ZHquNM/w8wHI90VE5uf4DQbZj1s49jx5/ATqYVffG+5++Aja3nn5FahnOc4PnjzUOXW3j9epNvKrG9f2Qf8eQtvZ2Qi3Pce6FHoddneH0LTd3oN6x4jTPtjntRWcjAOwAvaN0YZv3MmNKCkeiE++sccvAv+nBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkL+FGDEEIIIYQQQgghhBBCCCFrAT9qEEIIIYQQQgghhBBCCCFkLcBl3gHttq5MLyIyHmdKGw4H0NYRD+rz+QzqWZ5r28US2o6MavPtVqC0Z569Dm2jCI+xKlKoPz4/Vdrp6Rm0vX6zgHq/31Pag8e6XRERX3A/IgdXoW8yXW9+NjqBtlu7W1Bv4SmRbkf/QX3zCrR9/ATPyWg01200xnc2B/uOI3jsYupPl8A1fgd013PwdusENdStph1fz9EiKXE3PDyfEWjDwjG+fdaOg/+Cq+19ox9NbfguaKNusG03xk7q6q0vIiKxr/dRy5iPZYP9rjBi2Y22XuNPbrWh7e0Yr/vD976ntNOzI6N/C6i7AR58f+tcaY7haK9B9WI0xloVhY6Dlm0O4v8Poq71fFpteB5ePxf4ndVGA35PRCQy/PHZ27eVNvv2ty/VP9/X/pVl+gwWEQkMH0BjFBG5d++e0mJjLF/96lehfnKCzxc0h/O5jv8/qH9liWMcwvIpa14nk4nSNjY2Lvx7F8WPQqg3IFiNF2NomzZ43na2N6F+fKr3fOXieQjjFtTnc513dQIc17qb21AfTXCOtlgmSmvFuI1WhH1gND9WWj/AvlsKPrc63S7UG9H7yzHyov3ndqDekgjqZ/lYacEQ+24UYN9p1X0t+kYbFY4J0WYH6hLpuSqNuLIqRYXz27rRcbY08sSswDG5XWN/j3w0Fit3xH5TgliTG7ZpqXNyu2URZF0Y544Ye3q21PM6NWJpbnSkMnQH9Nw1Yq81rRtGTO75WveMsftGDrPZ0369aVxqPOvcMTqe1XoOM2N9V+EMxG8RfMb5Hr5j9Pp4f3d6t6AegTwj8vC+9wLsHGEMzk9j306nOI89H+tzWUTk+HSstPkc37Fj48zd2dPvFFf38Bm6s4PPowDGD5yTVRXunxfgNYtb+MxwwD2yKvB+9o29NRjosaN2fxBFhfNilOPX9dPfF8sEvwU54F5q5YOXBbVj5aVWXp6lOh4nKV7ruGu8pVl3b8DlTjNsXxj+VRrxznpdcB0Qv0Psd3WFe16V+AyogI8VFe732ULnnCIih7n2XV9w/HAbw6dBvvJRX7Q+Lz6Gf1deYr+rS71frbiRzPDeyhMcw3xHx8H5HMfuFngjERHxIr0HZkYbBwdjqN+/fw/qy5me+0Efx+5lMoV6Dd5Zr+7jd9P5BL95Dvr4fiXgrTawXKPCfjfo4jO+HWr/rY03bSuuVI3W0XyIiDRGZPFAGyIivqd19HsXhf9TgxBCCCGEEEIIIYQQQgghawE/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/KhBCCGEEEIIIYQQQgghhJC14MKVoVzv4gWZfat4VoIL2MxnuFB4u60LUc6NAj8vPPcS1FttXSjr6AwX4o5DXMRlNsaFY0pQyMg1Clo/fvAA6sONXaVd38MFt6dTUBBSRKKBLjYuItLp6uI47tEBtM2XuBjd+RjP1XCgC6ftbeNiak2DC4o2ji54c3SKCxTVFZ7XpsYFZRpYyNkosLgCoVEkT0DBnaLEtq0Y75fCKogF/G4JClyJiFi1dVulbqNo4X50QmOOrUJZoBlUUFJEpLYKjQV6/eII+0BeGEWpjYJdO10d9jYXRlFZHLLEhyU8RT4BCkG9tocLOKUJLoJ1fwYK+xpjSaxCis09/Ju5LhBVZ7ho1CpYxbVnINa3WjjuWgWjL1NI2ip8ZRWMRqAC5CIiNSj8JiKSgkKAIrgQ8fXr16HtconjIJqr0QgXYLbY2cHFjMfjsdLOz/G58LWvfQ3qi4VRuB6smVW8ERVDF7lcofDKKKZmgYqWW4XWV2GW4/mZno2V5hrzEIXYdwujGGcLFMttdXDO4BoF2sJA+103xoW1JxXO5yYFDqYV8APP+Pc2kYNjRa811CKIdSIieY5zSJwz4D2Xpji+9cFci4jExhnaSfU4ayM2NYY7bm7pHLIyivhtbOJCuNIzCgSCCtFRZFRJX5G8xDEP1f72HFy8szByvLg2io7WYP6NuJQbegL8epxh/7AimOvi+Y/APoiMguCVURJ2CnKsuXF2WYXCwTSJCC6u7Rv9qIycIDAKJQ8iHaOsmOwa+WUB8rRj42ytjdiXGwWek1yve2oUXl2FzMjP2m0UC/G6np4eQb1jxKvNDX0HDaycyXgaCMG6WmOZTsdQP3iM76vnU33GNI1RcLjG8SrP9fz5IS7YHBk5am7cuxLg66FRoN71rXk17kygQL1j7E9Lny/0/PmeUZjcyLerGu9nVILa+Rj+/axV5BjdNWvj/nTZAuJoRWoj10R3HRGROciR+32cS3lG8XbrTuKiBbfGaMR6F9yZrDOxtAqIF9g3mkbbxzHeF5UxRqto+QLE9fMZzrcP5zgXnTa6L64R4PIUt7FY4vxyPNexb2m8Ca1CNsNFqp1Mz0/Lx3nUzCgkPRzg86IC4xgMsU/HbaPwOoi9Jyf4rH7/g/tQ/+D9h1C/sacLem8O8Viu7A1w23fvKc3z8N6PN/Cb57V9/Ea6van7Nz7Bb691bhUKx/O62dfzuhxh3zWe0sRHbmq8reRGrLUCDlJB7fALw/+pQQghhBBCCCGEEEIIIYSQtYAfNQghhBBCCCGEEEIIIYQQshbwowYhhBBCCCGEEEIIIYQQQtYCftQghBBCCCGEEEIIIYQQQshawI8ahBBCCCGEEEIIIYQQQghZC/yLGoahB/WNzb7SHKPK+bIuoB7FuGp7lukK7YvZBNpu9btQdxpd+T2d4rHkAZTl4MkZ1APwSWh3dwfazmcLqE/GusJ9b8NqA/dvmZRQ92M9oKvXn4W2SXIO9dF0CvXxfKm0wMcdHB0/gXo70mt2dQ+vY2O4apJqHxEROXyi59V1jQVegXYrhrqTVUrLK62JiNRG2z52Uwkd/Td84/PkosC+IY7+C2WDTWtgKyLSMX40jLXuGW1XJR69G4D1bnAjHg43Ern4DzZBvOkHeG08Yw3u7GxA/aVru0oL9/eg7Xg+h/rx3Q+V1grwWMIaz0ljtB0B3TPWdxXyPId6kiRKc138+0Fwuf1aGfsL4Th4Puta+2Nj+J3RBDy3RETKUu/Fra0taHuZsbTbbahPjdi9WOCz6LXXXlNaGOKz2ZqTueF3aDzWGvj+hdMSsw3L/9AaiIgUhc5NkLYqCyMPaHUHSquNbekIHlsY61xHRMSNdEOF4LEtqjHU9zav6f41eO5PD/XZKyIyXeAcoxO3lJaneIzTXMcPEZF4oP100NW5qYjIYor3VpGlUG+3OkoLA3xuFTVuY1HifSFt3U431PMhIjJP8dgXonNiK6cYxDi/qmMcs9wMOGFltb4anrFn00KvV+XhPtTGmV85eM0zEMeWBY5tSyMcJDnI9Yxz2W/jfNGtcL8bcJcqjbGkJZ6/wtHxtHRwYlOC809ExDV0z9W61+D+dYy43u/0oD4r9NjnFV6E3NCbUvt1Y4zdcuuqNs5i4DvOx5BLbW4OoR6GOj+qa3y+uQHOV60zI831vC0NH2jHlk/r+YnbeO5ffBnfS28/fxvqZ+Ox0iZj/DZwZf8K1Pf2dF6O8j8RkbGRS1k5QhTpeXUsvyuMPWfkxQ3KDY1cdL7E5858pu/vnofz7Tm464uIlGBviYhsbGhfC3x8pq1CZW1YIzdFWPmjld9evGWRpjHOKBBPauO8sBa2LI23BOQbPl5XzzNiFfC72piPsjLyaeMsKgvwpmfcR6wsw/PxneR8ot+fxjPs/+clHnvR6D2azvC71vgJftean+L8Nwf+WrXw2bcKy9Ex1Oup7lcp2DcaI49yjEepMNBnQL+P76VnIxw3fLDi+7v43Do+xrF+o4fjzJ3n9Juq1+CYns/x2K9v6TbyAvvXcGNo6PqeJyLSauvcyC2xbyQpvmNsdPE5/Owt/cZwnhhvFCnedchLaiO+WUGyqY1DCtpeJtL+/8P/qUEIIYQQQgghhBBCCCGEkLWAHzUIIYQQQgghhBBCCCGEELIW8KMGIYQQQgghhBBCCCGEEELWAn7UIIQQQgghhBBCCCGEEELIWsCPGoQQQgghhBBCCCGEEEIIWQt0yXWDNFtAvdcbKi3PSmhbNTnUj4+PcNvtrtKu7m5AW1cmUJ+daX0rakHbaYErwgch/vbjgmruu7s70HZjA/d7sZgr7cnxAbQNQ9zvzcGeYR8qbZlNoW0tMdT3b9yBelUWWsxSaDvo9qFeNLX+vSub0HZjB89rbXyX++533lPawcEI2q5Cd9CBejlNlOaXDbR1Uz0PIiKeh8cWBtrv4sDD/cAuIy5ou8Ldk0laQd3v4P61wZo4hRETchwTJNChyfPxGF3gRyIifo0HFFa6L/ttPBbL76z9XGzvavHm89D2dq8H9bNMr++j974LbXf7eo+LiJQLvBfPHurY0jhP/9u24+gxiIj4vl5X18W/nxu+Udd4vdFvWm1Yv+l52Mcu+nsiIkUBYqOIlKX2u6YxYsIl+jccDqHtcrmE+uPHj6G+u6t99/r169D27t27UEdnjohIq6UDUZbh8zZJdOwUsdcdUVU4Zlm/iebbmr9V2OgNoF5Hel1LwWNIEry3M3Qmi0hW6HnLa9xGt4cPjFmhz84swb47mZ9AvShxDlmBs6ECMVpEZJzhMzxcgn3RNXyx34Z6XOAcqB1o+9kCjyVN8RpEMW67cUC8KbGf92Mcb1BemOe4H9NzvDa+i32t29LnXMvD87oqXeMcckCukha4v4WL5yhvsL/XRaS0sZGSzDIjVoP5aAT3I13ixn3j3PHA+VDUOC7VLvabvNZtV04Abd0G97sjeOytQLfTa+H91WvpuRYRWRa47XGtc4WFkdNl1tEAfKc24mptjFGM33SRbDSxCkFgxI5Gx8gwxOsad3Bcb4wcJk31fjk9PYW2eyBvEBFpxfr+XoM+i4iUxnltjf3adX3n3buyBW2tnG42O1faMsH707FikzF/KM+wcrq6MpwXNw1zaMvWM36z3dZ71Pfx/kwTnDMtl1hHYw/BGboqtXFhdcEZYK2TtWEdx9ChrdG24TPI1zPjnpKBfSjyg+4Hui+lh9fJ84znP9C2tT+tnDxAPmownY6h7hn+WAmeq9ORfuubp3js0wbHhDTXed3o/n1oO3vvbag3xjuiH+o94LhPP5cqFvj30ZvAwSG+C5YNXu/dK9Z7iNbnI3xenD7B75t7+1eVFlTY/3da2P+vPr8P9eef0f07O8P9OzocQ70utU+3O0a+ZCyrX+G1mZ/qs2i4uQ1te318lp/P8b15COx3h7iN+RPdDxGRBrzp1cahYzxpmDrCOm8vAv+nBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkL+FGDEEIIIYQQQgghhBBCCCFrAT9qEEIIIYQQQgghhBBCCCFkLdDl3A3CEH//cN1aaY2U+MdCXC09jiOoB67+zZ5RVv7B3W9CPT99T2nDFq78HvZuQD1b4LLtvd4VpV29eg3ajkZnUC8rULG+yaCtC+ZDRGQxH0P95LRQ2pPDD6Ft4HtQv3ELz8kzz2q9SufQNowCqGdFDlQ8xuViBnU/wL7z7DPXlTbsD6DtKkRd7Et+oec+zCpoW0Z4G44W2H461m1f6eI26gbP57TUPj0r9V4WEVkkaJ1EMmOt3EC33TNigtvg3/QKbe9U2Nav8Tx5Dd63m4OhbuPGLu5fbxPqC2Ney1D7Q+ngtWlv6/ghIvL6j/2U0nabKbR9PphA/RyHEPnweKm0sNL+tCpZhjuwXOrfb4x1qmu83lWF1xtRltjvHAefRb6v1+qy/bjMeCzbwQDHKtTv3V3su2gsIiJnZ/gsQrq1jnluxATDHmHNqzUn1m9eBqsNNK+zGT5zViHq4TWZZDoPiIMY2iaC/e7o7BzqrqvP9o1uB9p6KZ770eyx0sJ2F9rGht+lxp6rHL1Hw1YP2t7c3Id6Mn2itOUU5yOTHMc7N8A5kOPo+S48kLeJSNg1YpOPf9NxdP5STHFMDzs41+i29VzFPvadw7MDqB9PcEzISr1frgx2oO2qzBI8p4Wjz9p5hufTM3yvFRo5aKV9r8jxGrY93EYBcvjc+vdiRv9K0A8RkbzS8bQ0cqkqx3u3cfVvRg6OvVcr3MZzwzbUy40NpY0anJM/WuD9OHfwb5YgzhVinA2Nse9AfmkcL2J0QxzBccsFf8Ex8tlV+OD9e1CPYt2vK1e2oW2d4rb9EN+nQ6Bfv67vVCJ2nlGiuTBydd8xYq+x3lLpuGTlerOZzjlFsB/4Po6xjtG/y2DlnBbWeFDe6bi4bc/Da+MCe8+Ib5tb+A5U1RfPpaII+9kqWPPpgPPCtrVav0Ru2hhzb+yLGuwLK8Y4RlCqSnz+5eCNwfWs+IX1Builcfe23qQSYzw+6Etg5LmjMc6DxnO8n8dLHeQyo99TD59Rk5E+o5b334e2rvHeVRlneQHvRh/Dvys33nEacLbXhu+K8b5RF7jthx/eV9rpsc7JRUT6HSOXSHUe5RQLaHtliOPJcKjzERGRflvH71awB22dEo/97FTfr65sDaFtq4XPCytmNoX2jabGd+nS8Jn5ZAz18xOt90IcV6IG929R6PGgvFxEpDDeRar64r5+2bPyX4X/U4MQQgghhBBCCCGEEEIIIWsBP2oQQgghhBBCCCGEEEIIIWQt4EcNQgghhBBCCCGEEEIIIYSsBfyoQQghhBBCCCGEEEIIIYSQteDChcL7A1wUMkt1wSJUPFxEpNXCxXkGfdz2+2/rIt9OPYK2T977GtRfHI6VttvF/ZhmuFBWWOPilNeu3VRaXeMCLGmKixudn+vxREbxa9fHxXHOx2Ooj0DRox2j6JdjFbsy9AAUfNq9iYukd4Z9qB89OVXa3Q90wSERkUZwpTuzSF2hi9UEAV7fVSitAnegMGVa40KYowUuLPVggsccgTEPO3hspwku2vNgqQsCHSRGwWij9uHVCvvGBBQr24zxPN0eYJ92c1Ao/BKF4UREmi4utty+81mlldEQ2p5Px1AvQGEnEREHFOVczHDBs9OjQ6jvlLoo+J0reH13fFzwN8UuJTui+z0f4di0ClZRyaLQPtZq4eKMly0gfpk2rAJ3yMes37MKOVptI6z+dTp4XVHbw+EQ2lprMJ3iovOoAKXVPyuWWoXC0VxZ+9nzcKy4zLpbWMXdFwtdkM4qwL4KrnFebG9sKW06wYUIpcb7JfBx2+229qUYaCIiswkuNh6A88wpsP93jCKM8cYVqEuuz7kiw74Re7jfYeeq0kZznCvmRg262ihULcBnalA8XEQkivF5loEiiCIiNchTHMHruMxwnO4EoKi6sVU6LXwmhhVuu92gXAMX9lyVZYKLQtYg5gU+jj+usS6oSKaISAHqIvpGzhtZcT0DBS4dnJdmuA6j1KAotohIN9R9aYxC4Vah3wDkhltGgeg7IH8REenGWH8fFDI/nGBfnxjFjBPfKAZd61yhMs5c62xA55dTWwHAaKM25hsUsf44CoV/59tvQ/3OS7eUtrOLC6Wi4r8i9nmIzvFuF9/TrVygavT6WQWtrXq1jrHnskyvSZrg3CPL8RhbsY5j1vlsVZS2chWUp1n5zmXzSOTrVhtWvVVUQLw2YqRVq93KUTsd7SdGGvmxgObnMjm5iJ33It2yzTJ8f8/RIWDtT6Poc1UZbYP7VVEaBd2NWJWBNox62+b7S2icL2iuEiPvmszxPeVkgvUC+G9a4r0/DnCx6uVEv0nVRtHzOsE5U2VNlqfnpDRynlVAbxAiIm6o8/L+lr53iIj0u0OoL2czqOcgh79x8zbuoBFnDh48VFq6xPPT6+M3xStXt6FegCCG7nwiIlmOz5Hhpr53BdjNZct4Z53PjXsAmJMwwo3nxuNO6GOfdhv9/tSN8Ll1dQvcJUTkvSfa13MjNpXWPd0oPu+6oC8rnBf8nxqEEEIIIYQQQgghhBBCCFkL+FGDEEIIIYQQQgghhBBCCCFrAT9qEEIIIYQQQgghhBBCCCFkLeBHDUIIIYQQQgghhBBCCCGErAX8qEEIIYQQQgghhBBCCCGEkLXAaZpmhTrjhBBCCCGEEEIIIYQQQgghfz7wf2oQQgghhBBCCCGEEEIIIWQt4EcNQgghhBBCCCGEEEIIIYSsBfyoQQghhBBCCCGEEEIIIYSQtYAfNQghhBBCCCGEEEIIIYQQshbwowYhhBBCCCGEEEIIIYQQQtYCftQghBBCCCGEEEIIIYQQQshawI8ahBBCCCGEEEIIIYQQQghZC/hRgxBCCCGEEEIIIYQQQgghawE/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/KhBCCGEEEIIIYQQQgghhJC1gB81CCGEEEIIIYQQQgghhBCyFvgXNfwv/vm3oF7XFVAdoxVLt6yBvYPbcKTGuqN1x2jDpsFyrfXaGqMbQDnNC6V5xqcm77LdBlTGdyxrTjxj7Hi+jbabHOr7fd1GN8JtvPXOB1CX7j6UvaCvNGtt/pc/9yZu+wJc3pdWB/2ia/WjweuH1P5wA9o+/+LLUH/1k3jerl6/rbR2ZwhtO70u1NvtWIs1HuNstoB6muq9JSIymZwp7eAx9q933/4O1O++/12on5+dKM0xwodv+GMFVgdHNzMyPRUaw3cuwskoMf5Ej9kzWymhWjdYv8xWrCt0buExex7uYRSFUA9DHOvR2KsKr2xl9K+q9NjzPLtUG6578TPA8oGqwnpZ4rVJUt3HxQL7yHKBx7Ncphf+PZyXiFQlPotQO6Vh+yt/45egfhH+m6/huFEBn/Z9vE51iR39e+8+gPrAbystEjzH//IPfw/q3/n+h0pzXdyPvMbrejgaQf3GcFNp7Qz373x8DvVeW+/F7SE+W+YZ7t98voR6AfZAht1LGkOfHh1BfdBuKe3KjZvQ9vEhbqPt6zR+aMSgJ+kM6rVxwGzEHaXFLr42fPUbv4UbuSD/8juHUEdxDN4NRERAvi9i++pl7iSucZDfe/ddpQ1DfDZ84s4LUI87IN8RkcDXZ491Hllx3dIvg5XnwjPDyFYm4AwQEXl4Ooc6Onqstq386DKsku/8WXzltWs/9N/9X/zjr0HdAWewaw3B+ANrXyBzq21rB7mO9runsQ9FRJyPaa2elg/AdoyzwWnw2K2eNI329srodw1sP2pD21tjr8E7h4hIbeSuNThMGqON/+u/9xWoX4T/w//r28bvg/PC8Dv77oFpHP03aqD96a9arVzyVy/RhnH+/Xlj38X0H5gxy7hjmHsU/abhd5Y/oias7jlG0tQ4+E6ycLUeFDgo/G/+gy8bv/pn85/+5/8e1D2QE5ZGfEgyfdcSEWkb75gh2APT2RTaRmEE9RbIhVPjHlAa97vEsN+9ekVpjZEXFTm+9y0X+p1pNse5i+U1Gxv4jQ3dp89O8V2nFeDccm9jC+rlJe7NuZHjBoFe98NDnK93ez2oD42xl2C/GMsr/8f/1f8D/8G/Av+nBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkL+FGDEEIIIYQQQgghhBBCCCFrwYULhYtRVAUV7bls8eTmkoWyEFbhQFSs7FIVZX9ARxpQ4cguFI6LSdWurohiFQQ3pulSRZnM0lVmoTGj2CyYFKttVNxWRMQNdRHKm7dxkcx7B7jIaOrr4kIiIg6otm752V9WLlN+rDTWb9jHxXm+9KM/rrQXXnsD2u7d1IW/RUT6w12ou75e16rGBaayAhd2ygK9fp6HCyTlOS5+6sU4vF3Z0ePZe/ZVaPvCq5+H+vHD96D+3lvfUNo3voaL747G2KcvU6zM5uMsUvdnUxlF1DxU/NP4tG7Vg2vMsenfREULRexCWagIqw+KZImIuB72L6N+oggoymb1w4rHDjjPHKNQITr6rDZERBrgG9YaGHX2pDZjLCgoa/SjNv6tBVoH38drUxS42FtjnGceKjXr4HizCrWVR4FUrDT83APFg0VEPKPY8Gihx7bV3YO28QaO9VnzvtKasoC2RYULcTsNXpMQpWhGzjDs67NFBBcFr41C760Yz1OSGMXlgWsEDR57VmO9MZK6fn+gbY0A0m7hXCcEhcL9GPvuMDSKu8/xORyBouDLCs/rqliFN11YzNJKknEbdop88ZzQvGOAIrRhhIthWgVrn0aBYquNp9G2NU2wmLdhGxrnaOjiNUsL0Lh1B7pM8VgLO+H4CyUwzjgXzJtdKNyQLf0SBX2tfxmJ9stl3wbsIuSXuNtajSPfveResfcc8Gljsq24UpuFu3XbntUPY/SobbtQON6fleEQaOxPqwD7v4oH7vgiIg4odmvlvFbdeqNeLszfHGMH2PeUpzAXVgfXEHOWDP+y9gtcY2ueLlFQ3S5Mjte9bIz7GFj3yHrsW4HJGS7Q3QEFnK1C4a7xXhmGOActEp0/OoLPrSjCObwDXrziAN+xz8e4iPbkHI99ONBFtGvjLDobnUE9S/UYrRg0GAyh7tZ4PMlyqbQ8w3l2t9WGegPeLkREluDOm+a4ELwYbSxA4fjKcN3AuF/5Eb6TVKXeF4VR8P0i8H9qEEIIIYQQQgghhBBCCCFkLeBHDUIIIYQQQgghhBBCCCGErAX8qEEIIYQQQgghhBBCCCGEkLWAHzUIIYQQQgghhBBCCCGEELIWXLhQuIcqvAqufYUK54mI1LgOo1mfzbCGqllQ7BKV4lyziCf+zQoVSbeKgRkFgfxa2/tGESPXKHpkFTKCRdINUBEjERHPKKZTgyKhdYGLexapLoIjInJwqIv6nB0/hraL8Rjqe9d0gVARkbv3P1BaEOEiRyKfMvS/nKACu7dvvwRtv/LTPwf1V1//EaWVHi7glFbYj8ZzY2/5eqO32ri4kRfhEFSCYqS54V9Zjn23LHGxoRoUrLX2uNNg/9q7gQuLP3P7FaXdfB7b/vZv/gbUP3j/Xd0/o/CxXXTuL7iQ3KWKDmI/CgKjULODfaYBB0xtFO51jSJ0Yah/M7AKhVtVNi1ALPVBkV8RO6ajwuL2SlvnGS4GBou+ogLaIuIYZ5S1ZihmhUZFWavQnl/q9a2MgtJ1itsOjfOsArGlcnHbq5AasQoVXm+M+XGNFQ9auhCgiMhoos/Z5HQMbTe3dqA+3N5Q2sEhPqvLDO+5neEmbjvWRZVDo1j29rAP9QUoTl5YeVuBE9FuhM+oLNf2hbE/s2QGdd8o7n42HWvbFBdaj3xcfLrxdAyZZVaxdjyvHaMwJEra64/pbHGNmNJA3SoWits29UuMxY73unGruK5VKNnK1dFvWv2w2kb6ZWx/kI6w/CPycRvtAI+nKMAdw7l4EdY//YOVsXLDP68MKzLmxwFhDBXQFrH935ofDxUKt5q41NQ/nULh3iXutnaRalQs+3LFnS17dJ5b7yKO1QZqREQasJi1VWzcyN9QeLJ+z/YprKM5+VgKhRs+gLp12X1huQEuRn3JRmC7lw1Ulm8g0Zr7p1Gk+nLriv3AuHsbgcU+o8B+/jjvx1atcWPdfbC/pkeHq/fjX2PY1bm6iEgQ6vyxAEWkRUSKCuuu8WTsuNreMWy3tnahjvx0PMIFwd0a5wFlhhdlej5XWtyxip7j+4vv6vFYd4nRyQjq6H1BRCTP9VvVoIvvc3EL9ztv8H3Va4P7lZGL5TkeewrudI5xp8mNN5fRBK8lGrt1Jl4E/k8NQgghhBBCCCGEEEIIIYSsBfyoQQghhBBCCCGEEEIIIYSQtYAfNQghhBBCCCGEEEIIIYQQshbwowYhhBBCCCGEEEIIIYQQQtYCftQghBBCCCGEEEIIIYQQQshagMvTA4ImN/5EV6wXo3J5jWxFxHGsSudat0wdYCsi4sCfxP1wHVzNvTHsUbX5usYV6H2j2rwEoB8Vrh7vXXyazN8scryO0/EZ1M/PTg37kdKqNIW23XYH6jdu31DayQT349EHd6E+PsP2i3yhtAePHkNb+dVfxvpfMI4fQ/3TX/gxpX3hyz8NbQfb16G+kJ7SIm8IbbPZEuqVYF+vvUxp9x9+CG3z5RzqQV1rscHfYOsab4DAA5tLRAZbe8C4jduucBup0XbjhErbf/4z0PYXtq5B/Y/+4DeV9nu//U+hbVXMoP4XTePg+XHBieN7YK1FxDU+uZcVts9yFI9xG4EXQd1zdb8dx/r2f7l/E9DU+hxpjDaqqoJ6kWvdMc7b0MfHuwPGKCLSoKMciSLSGBNbGOefC8YeImcQEdFbSEREckePPalw/zyjbdfH9uiIrxu8BqtQVEYMAz5dGXNsuLTEUQvqV6/r8zcdn0PbJ+/eh/q1a88oLdjah7bTo0dQb1cJ1N1M+0zLWL/cyDHyWudMRliRsMH+byWXZyAnKYwcbX4+hnqS6zNRRKSKdByKjX70IpxHSan9NDP24TDE8+q7+DcT4G1ZitdxVRwrL4cx6HJ3CSt1vhRGLHTBb1pxswZx8Glhjt28X308NMbvefgyJt0I37umS7RnsK1jRUUrWF4Kw9eeRtMXIAyMeynogJmpGPvbcg0XjA73wrpj23dyw/hSbaMlMX/NzN9As8a+tXzAbAfdSYw8zWq6MXwX9dHqXWX8ZuPqxq1cuTbmxDO8DdnXVuMr4F5iPm3fuJSMf+6SIR3FYytG221bTnOZznx8Z5EFHKfh6M5lNr9cdh0ubmzlJbVxuHgutq/AW8e3f++3jV/9pQv1Df5OhXO/xbl+E8uM90A/xDlyYPhpHOu7h3V/iWP8ruUH+jcPnxxB26LA+Xe7hS+PHaC3Y5xPj5sJ1JtSr2u60HMqItIYPu22L55LLKe47RK8c4iI9AYDqOdgrnwP3wOyOb5fhcC+MjZc6Ft3XLxmRap9MAd3wovC/6lBCCGEEEIIIYQQQgghhJC1gB81CCGEEEIIIYQQQgghhBCyFvCjBiGEEEIIIYQQQgghhBBC1gJ+1CCEEEIIIYQQQgghhBBCyFrAjxqEEEIIIYQQQgghhBBCCFkLcAl0ZOjhSudVpauUO7CWu4jr4G8oVqX4RvRvOrgbUte4DafRffGMyu9W2xYe+At1VRlt42ruPhgjavdPG4dyY1ShPzp8pLT7H34AbR/cvwf1dLmAelWAivVJAm1ffOEFqD//0rNKWxq+cHCgxyIisijw2K9c3VHaaDSGth8Hnqd93fL/usL6m1/4MtS/9FM/p9t2B9A2SWOou4X+zaQ+h7aZBFBP3RDqfjFVWpGk0HaUaj8SEdkoPaWFMf69xNgXcabbEBGph1pbRhm0dYy1aWVYL0o9nsTB/Wh1bkD9cz/+y0prnAja/sFv/RrUy2IJ9T8vihKvifh6LrwKnxdVgWNmAc4cEZE013PvunidAh/7dAO+81cVjjGua+nGPhcd26qywLZGHHQ8PX9lgefP2BYSGP+UAfXbOs/qGo+9MNYsB7oxrfDcFxFxgO67eOzIz0REyhr3zwFnrgfmelUaY97QGW4c61Ib82PlQAEYR3cDx5PvPHgH6mmwq7QX3/witP1u8s+g3kxwjD2dTpTmGGel7+AzAPlj6OL180N8Jj45OYD6wdGh0obDPrQtSyNmFXifb+/pea2N/Lk09qILxrnVwfmAU+M1SDOcu01LfW6PJmNouyqudQ8AuuPgOXKMTWNEiUvhGtcl19VniXXHsPaoGHt6HTHn2hhiFOJY5DraJyvjUHMa47B7CjwN31mFODRyFRAjXevu7RmjMO6aHrg3X/pfQD4Fl7bm3kN/Yv2e1QiyN+LHZYeC9rljzKB5zhuxAt73jTFauRQ6L63fq40OWjuuQrlMZcW9FbAmDs6PcV5YbV/K/nLegc4u4+cu3baAdW3sxp/ObwKs8xnaGoHFegazlh22Yd0lzP1y8d+z2vaNt77z0yPd9kznvqtybuRns+lMab6P85Rerwf1ZInfFaYTMA5jko+O9TyIiMSxzstL4x2hN8D5bXeAc4kWeDuyYlKvjcc+BmPstDrQdgruNCIiUYDvL1FLj/10NIK2gYfzgXaE7zXpQudRWYnvAU2C7yl+rH9zaflCg9uI4xbWA71mvuB5ugj8nxqEEEIIIYQQQgghhBBCCFkL+FGDEEIIIYQQQgghhBBCCCFrAT9qEEIIIYQQQgghhBBCCCFkLeBHDUIIIYQQQgghhBBCCCGErAX8qEEIIYQQQgghhBBCCCGEkLXAv6jh9955B+rtdltprRauct4yKsVHIa5Y73rgm0vTQNu6qqDeCLZHOD6uuN6IA/Wq0m27hm0nxpXp0djrMoe2s9kM6t996ztQf3z3XaWNz06hbV6WUA98XfVeRMQL9Dh9F7tT2OlCXYB91eDvbI2D2/ZBP0REArDusfvn9w2vroHf4a7Km29+Huqf++JP47alr7SmxntLCrx+dVEoLaix33meB/WWg/fWMJ0r7dkAz/2kswF1H/hBXeDfSwPcv6DEvxkvpkpbLnD8mEfGvArez2VdK60IcL+rHDtEJDomfOnH/wq0DbwE6r/5L34N6jWIWR8HaZ5BvSj0mHMHr5OxXSQrcNs50H0f+4ZnxALX0etX4SYkCPDeahrjL8Dfw21Uje6HiEgB9Az43Ef9wGvtenhm0VlZ5PhcyAscKzJj3XMQb/Ict1EWF//NPMO/Z43dyh8c0XPoWg64ArXVL4Br+Ghj+EbtGB2udWzb2cQ52mfeeAHqv/GH7yutVeO59wocS4sEr/fjR4+UVm4MoO2etwX1ZaXbLg0/n52PoT5OcX4VtnSsz5IU2pZGHhqBNkRElmC/+B6OCcsG/2YY6fMiK/V+ExFJcn02i4i4hu9MM32+JEZ+uirATUUE59mWq4uR71t7CccD3Lhn6RW6pxh7F/dCGmvwon3BsQf/lwLHinHGOR+E+g4pIhK62lfL1PA9Y05qdDZai2BMq2NmIoinn1+18gX+AzA0xzFyD2NNPCNGomH4xh6qjLmvgL1X4/41jTX5uN8B0NGd4SOMfgPZL/HZajmHccWQytXjtFqua9yIj94/RKSEC4/jvdvguFKh+a7xHTtP8B2jdozzH9yZHCMnWIXGMdoEOfxlN/1ldrEZjw3ZhYnl5eKGY+SAPtBLwe9alZHgOoJ9yejJJWytJvDYzbc7K2SBGGf5v2fEmxq8M1VGP6x8IBlPcAerpZI++alPYtsV2BgOoZ6ler+mKd7bxlOjJEs9BhF8RsbGm2edYf8az/U5NzobQdvtHXwPMH0J5AFnpyfQtkIHg4iU4I20BPcOEZE8x7m6lacU4B7gCvbdfIEXZ2Lk5dt9fZcanZ5B29j4zcjTd4zUw+dFZORzrofjEDrOjGfnC8H/qUEIIYQQQgghhBBCCCGEkLWAHzUIIYQQQgghhBBCCCGEELIW8KMGIYQQQgghhBBCCCGEEELWAn7UIIQQQgghhBBCCCGEEELIWnDhQuHTx3ehvgTFQjyjOGurjQtGb2zuQH3Q31SaY3S5boziRr4u7lIZBa3dEhcsrypclCkIdDvXr1+HtpubeiwfoQvbLBa4MFxT4sJcyUwXPhYRGZ0cK60V4QosVrGryih8WYAir1ZxVmv+UIUYqw3HKDKIi26JoPp3f55f8NA4rt18Btq+8ZkvQt0RXCy1yHSRV1fwnqtcvC8cR6+fVcrNMf4kzrDfdUc6VnQSXJioWxlVv0K9t5oZ9v/M8OlJFxcm2lpo+8Io7lzfwvs5jYdQd0sQD43i3K5R1NH3UXzCY/nsl34S6sfHuviuiMhb3/wjqD9tClAYWkQElYAujL1dGQWwi8Iq0guKbRVGEfLaKiStfSOOsX/VRgVxK96h0FYZxS1LI1qVoMBdY8RAq17r0iiuXWSgELdRzLuyCoWneG2QP5RGwXfzzEGF2oAmggvDiYg4RiE5VFDWLC68ArVRELip9TgcB+cjqEiqiIhVa1WAr7vG+r380vNQ/61vfqi0cIFjupzj4n7+DBdQrCs99oXhvBNQ7FBExIn14K/d3Ie2338X57K9Ni6e7oGir1mC+9EKDJ8x1iwBRVjDyPDdCC/wYqzzxbngud69sgv12igs3u32dD86RjHiVTFyAR/k/K4RH2vDb1wjzuK8F7fhG223QJFez/g9K1+tjaKjqH9WjmzxcbVh0VjFWY14Ght7pu9fvN8lOLdFjGKzlyy0bp0lqJ2Po4R7cO87UK+Bj1lFp3Mj3ne7OObV4Ay28ozQKBjqu3pNvAqvkwtsRUTSBp/vBTgaAx/nyI6VY/lad2CRabuMs2vkrj4oOCxGXuP6urC2iIjfw8VwC1SE3NhzjZFvVMBTPeMO9OTeIdQd8LYiItK/2VfafGJUHl6FyxQKN/e8EXeNBUfx2zH2hRUMLnHkiOFeUqZGweZcn0VBZ9to3JCtwV8CK05D1bK9dDDV7VgjqY19XoB3EbNgc4LXIG1wrHU7OsZ12jhHXYXFAver1dKxPgxxzLR0qzj6ErxZJsY75nxqvBt19ftwr4tjo3Xvi1v4HPEDcBbBdxb7rRG5Kbo3ioi0jbdu626bgTWLjJzcNe5AiyNc+PzZOzqHL1KjWHuO18xv6aLvrQDfT7MMj7G0iqeDOanNN+M/G/5PDUIIIYQQQgghhBBCCCGErAX8qEEIIYQQQgghhBBCCCGEkLWAHzUIIYQQQgghhBBCCCGEELIW8KMGIYQQQgghhBBCCCGEEELWAn7UIIQQQgghhBBCCCGEEELIWoDLvwN2uwnUG1D93aoIX6UjqGfjCdRnaV9pZeFB26IqoS6hoyTPC6BpU2tbEZHAqPL+xS98SWm3nrkGbR0Ht51lupK9k2PbCR667O5sQv1BqMe5WMyhbRCEUG+sea21blWsL3LDd0rdhtNYbaS4G4avNVBD6sdD3G4r7Y3PfgHaRj28flWDt2dTaa0WPA9SFVB2vFxppW/sLdE+KiLSd5ZQr7taO55jH+gFMdSrRMeKYacHbUc59ul4cx/qp6X2pcc9HWtERJY9PCdOjvdFL9Xr4Bt+lxnRtyn0t+basO11t6H+Yz/116D+6N4HShufn+HGV6AEe1tE4MasS+DQIlKV2HfrWvuuiEgFfN2Kuwujf77bUVoY4MlPKrwvPBf7TONoPTfiV1HhOSkKsG9L3I/KGGNR4HmtwDpYbZQZXoO6NHQQ12sjHFtRugFr6Xp4rmtj3V3jn3EEjf6D0+nY6MkKGP1y0DiMiSiMPMV38F/oBDre3dkHQVpEttrPQv25r11X2sn5MbS9vY/Ps24f76N2pMd+vMDnxVkxhfq1qc51bmzgXOxuG+ebzvwJ1KNWS9vG+nwXEQlqvBelxntuuVgoLc/xHvIM501QGw6e6wWwFREJQuw7cax/s6iNRHRFHr7z+1BHMTyOcd5gxXsr5gkwD41c2BM87tMjnatcf+YZaOtaccnD62WN5zI0jV5b1/Clp/F7rmvEOMFnmpUf+eCcP7r/DrS18t+q1r9p3VM84yyxYja6+7qeccB85gbWL8DP37kCdTQMx/h3inmJx+yHRmIJznHXiGHWOVU7ui8l2nBin8spDoWSgSt8y8X+5Rn9Bke+ZI7xgw3ut2fEQg89rYB9KCIyATFWROQoxPertNFnT1PjN43UyEUdsC8iYx3nJ4+h3rKmyh8obWTsW5GfMPQ/G8eYT2hrOKmRMsFz4dIYbaOmUYz+yBZ3xMrLq0zfhcPuBrR1wf60+mdx6RcVME577E+hbaOV2sO/6Xk6fysnY2ibT3COur2B89/J5EBpGxtb0HYVGuMs9MD7jm+8+aQpzmMdHGIlBTlrt4/vGB54lxQRccD9eHOI52e2nEHd9/F5tlzqWGrlwlubu1DPCj0n4zFuozECS9TFb8lbPX3HiKY4/ldGv0U3ISIiV2L9bnblRZxT/NH3cZxegPt+6mFnmGc4NrkuzqsDsEUL433hIvB/ahBCCCGEEEIIIYQQQgghZC3gRw1CCCGEEEIIIYQQQgghhKwF/KhBCCGEEEIIIYQQQgghhJC1gB81CPn/svdnsbZl2Xkm9q9+t6e/59z+xo2+y8zIjkkmpUxRFCVKFNWVVAW4XJZlwX5xoQDD9rtfDBiuB8OGAdsvfqiyAEs2JFXSlNgo2SaTmcxkZGQbzY2I23en3efsdvV+CBk2avyDOsGdodI2/u/xvyPmnmvOMccYc604GEIIIYQQQgghhBBCCCFWAn3UEEIIIYQQQgghhBBCCCHESsBbxRNGpw+o3rS2y3sY8m8lQUDanAOo6zOqF4uB0eKoQ23DhOtJ0DdaFvPHXix4x/Wb125Q/ZXnnrHzcJ69rnmn+Di29ouUzy9fLKiedXpUv3TNzu/WT35AbcMgp3pZlFyvbIf7quRd75uSr2uAhhjzMfb3n1B998oVPnZg17UlvvpJ8fxLLxvt8o2b1PbKzWep3rTcp+/fs2vRNikfoyFrDCCA3de25T7KRwBK518eLqyeDy9T20mnS/Xu3btGWwT8XJx1Nqk+PImofgD7m+XmRWpbLxKq9xc8lrWVXcNFwn26rrg/NiQsB+DzSLvbVH/ptTWq/9yXf95o//rXv0Ztl6EonbhR2FhQO/EBtafz9WTETqwvQr5/JXHponDiRszHaMD9rmJjV3yd8tyJx6XVaydmejmnbRy/I7HCix+tl+ca/psR2Ycw5jHLm3dEpt22Tnxzao2C5C0AeO/BbaPdfWBjEAD8J/hVqp+HuuXzqojeNNwHYhJjAGCHLydevWnj4/VtPo8s5r7xC1943mh//OYdavvZz/4Fqv/J1/4V1bdCuyfJFs8Lswmf905mH/5Tz+9S29MpryV+XPI69P2nh0YrnbiCgtdoHVLnAUCna58zSXiszydTqrMVKZzcfO/xQ6rfeGaPz4/MpTyaUdtlef/dN6keRTaedjJeG9HFAFA5534+nxstyzJqm6XrfIyF9YWrN5+jtiwOAkDkxFOGF9s8/ePg1cje2CHLo61z/3PyDinVAQBNa/fsnXe+T22n0xOqs+dh/vRn6bXjOwWpZWLn7P7qf/TnzxnDDp9XQJaTaQBQNHyRE+euGZK8nzj/D6SX00pWrzv5BQF/xjznOjtGXWf/Ys+nSYxsK74ebcnHaJ01KYlTR04t9TTl8z6JnPqysrkucOYR4Px372hhYyEAnD7m737Ocl67npU2Bx7d+TG1XYbAq/3ACkUnZjJb+PGOhjDvvYJzGNnI3jy8oROn/Ahi4tM1ryODiOc5ei68dXISrluXkwdaPmt9RMPm6CWXktcw8fTAaE9uvUdt1zavUz1y6vC0tedrsbC15dI4jzwc2Pepnp+fjnktnAbcZzp9+541Wec1WtvhE5zMbNyIQ55PPX8sCu7r7Px7+d5NouSdWRByP48Sp16KuH1I5tcP+SH33rv1BnYPAGBr274jevXnvkptm60LVP/Wj79ltKTjzMPJleWM2xcz8k7DGeM86C81hBBCCCGEEEIIIYQQQgixEuijhhBCCCGEEEIIIYQQQgghVgJ91BBCCCGEEEIIIYQQQgghxEqgjxpCCCGEEEIIIYQQQgghhFgJzt0ovE5500XadNRpwto6DV6rBW8KsiBNqsOQN8kMQ94gJk2t3nEaa6+v8YbDr75imz4DQI80FKydZmDe16M4Ig1UScMcADg8OaV67TRQvnDZNjJ6dO8OtT3a5w0kvUZVrGm518gcTlMf5iZxxFdqjTQiAoDA8TWmNrXX8vrPz3Btg+qvvf55o7Utb346mfB5xTFv8taQxl+t06DXo63tnqSN12iPzy9zGtxde/EzRjvN+RhlwscI123DorbDm1Tt7m5RPVnweBOSJkRDpyHh8fGEj9E4jYhJX6uK97pC5hyukESLiuwXABwf8eaxiylf7y/+/C8Z7a3v8+aby5CTBqwAULJGXi1vONhWPJ40TiPpkMTBwGkGlsTcl3IydD3n5zDr8TG8PMdSQ+vlC6eBG8sXjdMovHXWyYvHtD+a1yM9dJqsOY1H7z6x+SVNeUx/5so1qrNG62w9AODgjDeO/Vff+DdUz2J7SP/iZ79EbZfBa4I+L60ege/rBfAmh83hEdW/8fAnRpu+eJnariU8/14jDfu2X7HNwwHgwT5vLPqln32D6q+c3TTaNz/kzRknJzzePbNnm+FtOp3TX3mV+9fvfue7VA9Kuw9e4+ko4Oe5dfJFv2+bNzYljzenE56LYuK7WZ83aaydHL8ovEa41i/7Ga+fl6Ue8Dq7JU20p94dw4lXUcafu03tWldkPQEgSXm8bzA2Wu00YQ2duOmUvbTR9cdt5n3ecf8s/LGtHrZObnDOQO2cmcG2vY9deOYVart4ZPcAAELiO16D0Mapc0Pn3sDqt59Gs/b/Nieh40skl2RO/PbupSB3WAAoctJg3Tlzi6lTH5F3A2upc26dseHUCOw9QBM7zcad2rkq7BisxgCA9Y0h/wfHd+vY5oeIpwz0Qx5PO7nT/Lu1AyVOoZbVPGecnhwb7drODrX9W3/556j+/R++SfUfvvcDo9VP71HbZfAahTvGXHbaVHv1NyuIG7dROJf5rvIxvLFDp1lwVbN7F79fBa33+s+O7TUy92ANwQH4CfqnQMhypdPIGRPr/wBw9oM/MdqDt9+ltvkub/L9/MWLVL88sXXx8YyfT+AfOfq/m9Bpjp6mth72cmESO77h5JGNzQ2jTWteqz98xO8pF9ftugWBE9NzfgfyvOvKlatGS1Med4+PR1RPErt+Fy9dorYhidEA0Gn43nQX9oxmTnw7mfB1nUY8D8d3PjRa5eTKTsDnvZnZmnjmFK3FlO9N7tQJrIF4njsN38+B/lJDCCGEEEIIIYQQQgghhBArgT5qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrAT6qCGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLESOC3uLVU9pHpZ2a7tTc27tpekw/tH9lzPUtttPs34d5gk4d3cq7Y12iKfU9urV5+j+ubmBar/8NYdo52ejant/uEB1Tc2N4yWkk7zAPDo/n2qHxydUj2AffbE6VhfFbwzPQJu39ihEUbcnVKyjwDQkr3pdjrU9qUXX6R6Tsb4aOzaaDXx1WW5dPkG1deGe0arCr6vD+8fUT0IA66D6AE/c2HAxyCugcax7XX5vmZdvvZTIs8XObVtcr4m9WDbaN4ZT5BRvUi57y4q6+ve/NrAiVkdPnZI9mHNOXNJwNd1nlvfbWqrAcD4dOHo3P7i5R2jPf/a56jtMjQFX0+QONO0PPY0DV97FjcAoCXnpWKODiCIuN+FTA/5/gXg/hiAn8Xj/QdGe3z/NrWtGz7G2sam0ba27Z4CQBo5ObGqqA7yPGHIfTSO+fplXa6/9c2vG+3tn7xLbf+L/+x/QvVLl68a7fZDnhO//u0/ovpOf53qv/ilnzdamvC4sgxJyPc1CayfDhzfbUcnVD8dz6j+zR9+YLS7t59S2zeuDqh+cWj19SFfyytXbO4DgEvXLlJ9MbHzvvj8M9T2/qPHVF9LbLzrdLn/P/PsTa5fu0z1dstq37/Dzy2rNwFgnvNY1u31jFYXTp3S8JieF/Y8D/pd/ntrfaoHTv1Q1zY2r/W5jyzLGXap3lT8uRmxE9edo4QSdu0y51o0q/ggdTmxP9c4MdbJJR4sD7QkVnz0D85Pwq6JN4ZTLQKOX4PkaM/UvUs0/Fc7Xeur2To/oyf3D6keNvY365L7k7smrVOHk3q5cfL2Mjw8sv4FANXMxs1eyv3/hZs2dwLAYsbHvn3f5ocg4Xezxrk7ru0/Mdp73/gGtY27PNe+8Pf/LtU7V68Y7eEJvwdPc+fckmnXLa9brzt3xy0n3GSJtQ9Dno86FX8fceGQ5/mS1G+p47uVk0u2iH6xy+8Sz65RGckGv9d3L9uEWa57QWEJvDjITL1/8O4S7AUHAJAz716xnVDgxm/2c940nGCfZaSG93K7U0+A3V/cOTux0VvXc4/g/0vtLCy9F5L3QADw+J0f8LH/9E2jHT/gNec79/j5zIf8Xenehp1LAueevASRc+87OLDvID3bXt/WpQDQFHw9uz0bCzbWeZ2Yb9s7LADEpa27jg/4GifOe+DNLf6bUWx9w3vv4L0njBIb7GvnHHadOjSd8BjbI/F4fMJrmqblezZd8JozIO+eBwOey3e37Xs3AHh+1+bbe2P+rjvg7o/LuxtUj0Ob+3/845/wQc6B/lJDCCGEEEIIIYQQQgghhBArgT5qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrAT6qCGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLES2HbzDhcGXarXte16n+c5tc15Q3infzwQx3Z63S6fBwLn+0xox4gQUNMb156h+mjEu7z/xu/8odGORiNq+9YPf0B1Nu1Pvfwstb13+z7V79x5RPW1tb7RQvC9qRu+C3VTUh2BXcNe3/4eAHScPWtq+5t5wX9vNptTPe71nLFro7Wt1Zbl6tXrVE8SO6+y5QcgdHy3dvy0Zd8inUMUOmOw/aurKTUdxHzwF158ieoffnDXaMfH/AyFkbN/gfXTIOTzOE7OqB4EDdUR2fWbl9w3oojrl3a2+NiNnffR4YiajvOM6nFo570xsHEWAMKIj3F4fEL12TQy2gvPvUxtlyEvFlQPW/ts7KwCQN1wvW25H4St3dem5j5QlgXVk8Suc0T8BQD2n/C4+/DOj6j+1rd+x2h333mH2o7ziuoBibEvvvQqtf2lX/pbVL985QbVK7ZUJH9+pFs/AoAk4jHub331V4x2cHBMbX/9D75O9U+9aP307Q9vUduffeVTVH/t2RepPiH5pS64jyxDHHDfzSIbj6MFP0NlzvWo5vpabPfkyZifi4Oc54vhpj0X84L76OPFKdWPHL1DXGx9uE5tn//Fr/L5De0zJk7eqir+jP/o7/8q1b/2z3/TaEHDfSN39syLWUNSo6VrA2o7O+PrF3ZsDsicWizK+PnMeBpBHNrYV/30yygAQNeZc0tyRhTx+JMmzoOEfM8L2HozcYqpwPl/wMp6Ym2dutm76ziVCif4mBsQ2APm1UaBUy8GJLd+ZG/HaZ2xad0KAC3fyyiwvnpp7yK17d/j94OahSgSawGgBo9ncJY7IDX0J8EXv/izVG9IjfXk+29S20ff4/ozn/881T/3eVsjBAGvQR//9m9Q/d6//JrRDn74LrXt9TtUf3fEa/vX/2f/U6PdeO55ans24WfxbGTP7dk+v2P/5Ld/m+qDN7/H9bGtv1twP2+cfNQ4d2G0NvfULIkCKKfceQMSxPdbPkZNamIAWPv861T/T/7B3zNaHjlnawlq5z4IUvN773y8/6vXSddo6Zl3Yqanf4xg7+WLJOJ7de3iVaM9OOS1SkPyKuC8SvMWxMGLjDxm8nl4Wa5x7iRxTN5pzEZ8jAm/Hz/Yf2i0ewseg8qK13rjmr/vekre/9x2arpliEjNBgAxqZnmc543Ox0ej5OEr/3kzK5RGfBazDsXoyO7J4sZf1+ZOe/Sqoqv/WJhxzk9HVHb8djmBQCYknvX1vYmtd0cDKk+JLUYAAyJtwdOjRt0+f3g6aMnVJ8U+3Z+62vU9sI6r682+xtGe3DK14m9vwKA6ze2qZ527ZqcTPj8zoP+UkMIIYQQQgghhBBCCCGEECuBPmoIIYQQQgghhBBCCCGEEGIl0EcNIYQQQgghhBBCCCGEEEKsBPqoIYQQQgghhBBCCCGEEEKIlUAfNYQQQgghhBBCCCGEEEIIsRLwVuyEG1f2qB4EpJM90wDwnui+zsaua9slHgBOz86onqSZ0bI4obYXtzeofvT0EdU3Bh2j7Wxdp7bFgneKPzg6MFqaptQ2SriedbpUH6zZDvIhSmo7X8ypPjs54XMJ7fewmGgA0NTODpP9PXP28ej4iOpX1u0zAkDT2N/kXrkcO9u7VI8CuydFzf2uCfj6BM56BoHV27blYzf8vLCzFYXcdnR6SPX9A7726+tDo03G3O+Kkq9JGDKdr1PbOv7VOjseWX2wbs8yAPQzHiK3NjeofnLw2GjloqK2YdSnei/LjfaXvvwatT2d87G//vt/SvWqtL6ztclj+zJUjfPM5FyWOY+NVcXH8Hw9imysb5zv9m0QUT1Lrd9F3BR37r9P9W/9we9QfXK2b7QhySEA0I/GVD/Mj432w+9+g9oWZ3yMv/0P/iHVbzz/sh2DhwTUTkxvA7431y9eMdrnP/0Gtf3tP/g61Xc21o321/7iL1DbixtbVJ8vCqq3oY0J4SeQMTZSvqBVYPWz8YLaHo54rg7mPEeWhR0n2rRrCQBtyffv/Q9tDsideuRgMaX69W0e65+/bnNl6OSiLOGHsajtvLtDXhcNMz7vr/6VL1J9dGxroLcfPaS2D/ftGQeArMfnsrO7aW1jnnO6He6PJYmHUWJjIQCczXisjZ3fXMytr0Vewb4kCYm9ANCyWs65Y8DJDV4uqcB0/oAhuO+xfBQ56+ndjUJHD8jYIX9EhM7GBKGtvUIS7z76Pb4HUev8/2/Evm15reeFUy+fMzbXe1TvpvzZJ7X90cb5Oc+lAmet2LRdv1yCbMBjdVNYHzv6/lvU9uFv/DbVew9sPQEASG0N38157jz+F/8PqlexjeEb/73/lNo++N6bVJ/+Mdff+B9bH1tb26C242N+f3/ne9812rWLl6jt3patXwAgf/dfUj15Qn4z5r5befc8qgJRZO2LHj+3Qc3jXo/EVER8jJlz9ie371B90dgc0+nbPLcsoRdnyJ3Xq+W8O3br3R3ZWni23m96sZT+njOy85NZx9Y2QeDUvE6tTh/HzU+ezufH9Nqp9aKYr9PZI3vHBoCTOx8arTix79cAIHHeJ0WkBhmzswIgynl9XsKJk6G9681SHtuX4eyUv0NjxBGvU/afPKH6oD+g+sb6htGqM+4EpVOLTSZ23YqKn/E6cd41kjUGgDiz76qSHr+n4Izva3tm/aA75757ZZPX+xkvy5FPT4128cIOtT2uuD/21/izd7bt+51jJxe9P7XvngCgE9ral1sCs5zHhNv3nHfJXbuG6YC/WzwP+ksNIYQQQgghhBBCCCGEEEKsBPqoIYQQQgghhBBCCCGEEEKIlUAfNYQQQgghhBBCCCGEEEIIsRLoo4YQQgghhBBCCCGEEEIIIVaCczcKbzq8oU1dOZ1ECWHLG+jETiNW1rzQa8SWOR1Y2Bip0xxnfY03wbm0d5Hqz7/8iv09p8n3X/rKl6k+Io2xS6c71N27vEHSZMxbtkyntlHsbMobwz159IDqf/Ktb1J9PraNbYr5jNqORiOqL0hz8sjpyhs6Db08nTUfPH87wvPT6zkNbVrij04TxtZp3h41Tise0giNNQ8HgCThv8nORcPmDKAiTVgB4Nb7H1B9a9M2hdvdu0Btj0e8aVRRkfk5zaeriscVr8HzWt826O46jfZOTqyfA8C7hyPnN8n5zzaoLWvgCwA3n7FNjj/z6lVq+zt/9DbVq4rHoSK3ftLJeIOpZXh0/z2qX7hgGy7mpBktABQF39fAOclhZNczdnymcJq/sfMydL79v3jzJaqXx7ep/v6H1iEHTnPcxmku39l/Smx58998xGP6f/V//d9R/ee++stG++ov/Aq1TWM+by/KvvnO94z2zns/obZ/7y//Dap/7tVPO79pKUoeU1un+WBI9tjLRcvQ1Lxeurxh/bHj1Dq37vLz2kt5nXK4GBntqtPI+Nk9XueVE9tU77sPeKO9ExJjAKBy9mQwtL4U93hMePjeHap3Bjamo9igtqcJf/bRiNcvr7/+stH++3/7r1Pb+095Mzw4ufy9J/eNdjzl87j6DG8cWJNlnThN5ovKaUrr1A9tzHzwk/l/oeZOHmBNtL3mzUnI41Lp5IE2Jo24nZrSa6rZkDjxcetVtykwsY8Tntu921wNG3PKmue/Kud+mk+5P1WlXdedHX6PYg2O/yxYI+4tp1H45pA//KywzxnFvM4NnTq8dupfNj9vf5fCuWLnU7v2t9/mtcfZD29R/Q/Sf071zccjow2c2FZcsE3FAWD71S8Z7X1SewPAt+/do/rfcOIV321+ivYP+J13vrC+MX/K/Xz+hOe6Z17/HNU7z980WpM6TWxTxx+d8xKQeOM2zq15Dd3M7bsBz882S76uxy3/D7pze46627xx7jJETg3fEN17b9TyIf6MxthkDLdROKclPxq4GcCZR8AnnqWsjnVG9h6ePGTjzo/TNjxmBlTn8zg74s28P/yBvUsAwIM/+WOjJU7dNWi5ntX2OaOAx6ykw3360mVuv0eabN/4zM9S22VI1niOLAt7Lr23t6nzLjSMuB9MZvY9SSfjY5wekdgDIICt3frO/Thzzj7O+Ngt8d/rGxvUdrvhsTTb2DXaxXX+/m/TuV8FpOE2ABxMyHumjK915MSm3V3+jq0kd6OJ06z9vfu8fqha6zuF9z6/y+vTNnL2bGG9kL2fPC/6Sw0hhBBCCCGEEEIIIYQQQqwE+qghhBBCCCGEEEIIIYQQQoiVQB81hBBCCCGEEEIIIYQQQgixEuijhhBCCCGEEEIIIYQQQgghVgJ91BBCCCGEEEIIIYQQQgghxEpw7hbjUYd3eW8K2wE9CPm3kjSyXc4BIGq53rat/T1YDQCiLOPzq22X9yDm3dmDmI+xPtzgY59TA4DuHu9Mf2Fn3WgH4zm1LUq+XffuPqL6eDo2Wqc/pLZXrl2n+vZ771D9g4PHRgsCvu/T6YTqjx8/MVq1mFLbIAio7tE0xKeIPy1Lt9elelV7nmAJA2debUnlpiZnzvG8KOE+3YnsGWiaiNp2e32qf+Gzn6f68emR0T68b/caAHbWLlL9dGTPwLRcUNt+33nGIZ/3LJ8Z7ejxIbUtF9zv0oifoyawZ7Suc2q7s8Hj0M984VU7RmP3HADuPbDnEADCiPtlGFpfa9vz++p5ef+d71P97PTMaIOud4acfBE5+SXrGC0D9+kAfO1PR6dGa1vuA01ubQEgP7P+DwCDjp1fmHEfPZvb2A0AZdQz2vau1QDg5JSPMTnh+u/+1r802nCwSW2vv/gy1f/VH3yd/+bE5oCvfOZnqO3zV29QPYLdhyji+xuHXF803NdrcgYC8nvLcv+M+3RY2TolXPAzn4cJ1duQ12jD3ZtGi1s+9k/u8nhy+dK20SYRn8daxOP0/JTns9/7no31z21xH/3UDZ4r1wtr//47vHYZbPJarCj4vIcDG29+/ssvUdtpzn0mqPhafefHPzTav/iN36S245KvXxra81/l1p8AoCy9Wtv5/5tI3dVGP/06CvDPMqvbQqfW9OpErzZlFXvo5Bdv7JDMO47PfbX6aBZObRqScZ4e8Xr60eEJ1ae59evFnJ//Ys7j42LG6/JOau2/+vNvUNu1Ps/znjexUN1J+brubfN67GBi5x0mfB6o+Ey8q4frrz9l0pBPYFTb+Sa7u9T26fZVPvbnP0v1xa/9ttG2c+4b5Vd5Hl9rbZzNYz7GLObxqnHyOLsznZ6OqO2PfsTzQBvaGnDe43H69ugp1cvvfY/qe8f2PpE7/tKPnFjhOF5Jnj1xatSw4eual/ZcRDUfI+bXFxzv8Lpz8+//qtFOh7buB4AtPvS5aBu+bg2p21qnlouc6NOS90YAEAR2DwPn/w12r1X0J7285eyrM3gntvNLyJ0P+DPeu5HA2zh1s/fssaNH5HHiktu+/yE/c5XznuL1r/y80VJw5w2c83zw7m2j1c781rf2qL62w+fXie39L1nfobbLkKXeu1C7h63n/049krT83szubGvdAbU9Sg+oXpOate+8O7nQ5/PYHPKI0iHvBjyfrtf5GOydYrXgd4aw4vPupnzea+u2fjme8Xou4NcArBH/AoAPSS46IvdNAIicu2WnQ3zKqZfC0FlX5/3d9Mzqsxmvcc+D/lJDCCGEEEIIIYQQQgghhBArgT5qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrAT6qCGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLESxOc1LKa8E/vRoe1kHwQBte30+lTPuj2qDwaDc2kAkKa8a3saR0aLQ/4tpw6sLQAU4F3eo8Z2kHceHYEzRgzbKd5bv7Mx7wj/ZP8p/1Eydhzxsb3fjCK+VnluO9Z3O3wfw5Cva76wYywmU2oLOAvr6FVVG62u7Xosy+bOFtU3dp4x2oe3n1DbOrdzBYCgt0b1NLO+3hTcN+LY+igARJH9zbCcUduhs3874SnV+0Nrf2vB53e2uE31G1vrRqudeZw5Y48O+blYLEqjhbH9PQCIwGNWVfG5AHOjrPfs7wHAX/jM81Tf27U+9e7de9T26amz750h1YdDG4fWUv6My3C2f5/qDx89NtqNq1ep7YXdC1TPGx5Lx2MbqzY3d6ltFPF8sahyawt+PrOMx8bI8dNsYc/L+PSY2k6Oz6heTewZHQc8J3qxu+vo04n1pV//2j+jtps3XuS/2ec54Jc+/2WjXbtyhdqGMS9LOmmHGPNnaRoe65uCn8V5buNkVXLbZTgu+HzPCuvT3Yavw2jOz/zG5g7Vt6/aOLMZ89h9584Dqn/zJz8y2iLrUttffOkS1Q9HPFffW9hxTh+Oqe10xmP65Y5dv6DP45oXM7/wHJ93GNuxJ45v7Dv1y+0P71B9Sux/5Stfora3HvKY+sPv29xwNrW1FQCMpnxds4ScLQBVafNZ0imo7bKETg3atGRvHVtPT5yYwtJA6NX7Tlxn8SqOuG3T8FwSd1Kqjxc2H33rzfeo7f6J3at/OxkjeTV5Gnmxl88PXbtWZctjr1fBw1lvtpWZk7t2d3gOTB7b9fNuAZGzZ4Hzv/4F5B9CJx8tw8EDHvOenO4b7d5jXk8/jXm8uvl4RPUBuQsXFd+nk33+bmCnY31pbZ2v8RB8fnnCvaYO7Fzu3+XPHjo7vpjbGPnhma1PAeDk5A7VrxzxfDk8svWbd2Pwqm/XT4nmvcjh0QZg0Z7vrv9/vnYmfG9uP3hktGNWuwF41Rn7PEQLnmvZu5aQxEDAX5+qdu7NbKVbvkKBs3IhDSh89dvGiZotn18/tmN3nSEWNX/6MCL5tuW2TcPP7XTBc9GCvPPZWd+mtlsb/A5bBTamA0B/MzNaFPD5NZmTcw7sfez16y9Q20+/9hmqP/idf0n1SWHnPSAxaFmCnD9zL7I5PMvsmgHAZsrz6bXhZaq/+oxdo37qvJvbuUj12++9bbQZiaMA0G95PbLlvAdmNV1e8Dq2brheEV9vKm47K/j5bJw7096efU8RH/MMcHzA16QT8mdfS2zMejLnY3jvHMcLe29onRjU6fF51CW3j2Ht44D75XnQX2oIIYQQQgghhBBCCCGEEGIl0EcNIYQQQgghhBBCCCGEEEKsBPqoIYQQQgghhBBCCCGEEEKIlUAfNYQQQgghhBBCCCGEEEIIsRKcu1H4w0e2+RMAnI2OjHbpAm9YWc6c5o8j3mjsvSPbtGdMmpkCwN4l3nxmd882m716iTeELC7tUT1oefOUgLS+YxrwZ309svaR02jvbMqfPXeaog5YM6TaaSYY8IY3BWmcCwBd0oTzZ77IG1xu7vBmvQFp3O08OgrS+BsASA9JAADrlVSWXiu0Pz9J7DVHt41unB6RqGs+r8Q5nmls195rYplPeSPi/tA2btu+xPfp+OAO1esBb9a0u2XP3PUZ96Mfffe7VC8PbDPMvrN+3QVvQPSM08Atieyzjzojanu6w5sZn67xxuItaWz42Vdeoravv8wbZJe5PecnJBYCwLDDG/D1O9wv19bsAeuRxnBL48STx09sI8ai4M3S+l3eLCpJuH42sbGtbbxG0jxOx6RRKms8CABrTtO0wmlidvDENvxsyF4DQDHj67c9sLno6ITnzzE/FuhnvJHXPLQH7M69D7itE6f/8T/+z6l+6fJ1ozWR09TXa0pL9qZ2EkBR8YdfOM3h8pn1nTL/6Tfxq508tiDtP88q3qzv1ns/pPr1Pe5Lr3zmC0Ybz52KZI/vSY9UMCOnKe17j/m6zRuuX7l0w2hBzp8dZ7wx60lF1u+UN8MrE95I8cxpSnjw1MasYZc3/Lt3zOvk4To/c59+42WjvXiR54Uv1rwe+L8c/zOj/fAWbyaNgI9xcjTi9qXds2zA129ZvB7LYWh9svUalzr1t1cnhrX9Ua9Bb+g0qY5J/V3NnSa2NV+7zGmkO6/s83S7fB5XOryxaqdvx14b8By63ud1w6DP/b1PmkGvO3VhRPYRACKnufvkZGS0To+PfXGXN5vtJQdGy+dOg1Cnk7MzPdQVaRzq1JzLMJ6fUv3Bd75ltMXvf4PaXtnm83rp3e9TPalJY9AOPxm9n7xP9fXirtE2nEapf+WUj31hg9fZZWPze1M7uX3M66P779p5P3z3x9Q2PbFN2QHg5zK+rr2udaY85bE3mPC7kXdeepu2BmxOZ3zsgsfJhEx7kfH5zR3/7zuNaZ/csnXujxse3/4+H/pcHPyA+3pKYlXhNP5+PON+l2zwd1i9oY2xEWnEC/gxPUusnmU8rsUJX+OCNJ0GgKf3bX1UjJ1mxiM+xil5p3d2YGsgAMgX3O9Onfw3JbnyL//yX6W2N17g7yMu5/x93L1bttF0b43bbt54no+dWfuzLf4OIA74nj2JnSbHpHl6z/GdZRgMee1cElcPW5701tb4GJf3+HvWB0e27j07snEAALrOO9xj4mNxzNen5xRpecMLvWpOftOpCaOAx4oQNt+3EV+/sMN943TMn70hTcgr51nKgOuhk/9YPZZ6JXzLY0LcsXFoPOW2jx7zXLnu1KfrmzbHx06tfR70lxpCCCGEEEIIIYQQQgghhFgJ9FFDCCGEEEIIIYQQQgghhBArgT5qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrAT6qCGEEEIIIYQQQgghhBBCiJWAt5YnFF5X+dZ+FynbgNoOM94RPuYN5NHWC6OdHD3hY/f5o3z3zk+Mdmttk9pub65R/eLeHtWbympxwucRBnxNmFyVvDX9dHLmjM33Jont4FXNxy7zmTM2ldHpdO0YZUFti9mE6lPynPv7T6nt2XjKx5hYHwGAoG2MdnJyTG2XIc/t7wDA0/c/NFrT8MXsdLjPxAl/5s1hYrSN9T6f37ymerGwe9JN+fxee+EVqj9z4wWqDwdDo/3y+kVq+/zly1Q/OXhstHCRU9vrW/w8ZzU5oACq0ZHRzian1Hb9Z75A9eDyNaq3hT2LF8hZAYAI/Lwgtv6wu32Jmj57vUP1uuV6mNrzMj3hfrYMNT8W6Ib2H46ePqS2P6r4+gwGA6rHaWa0p08fUdu9Szeovrtr1zlwPv0n8VWqX7zGz8XZ8YHR8hM+v7TH43HS2DWJefjH6ZjH3Qc8jSBo7XmpWx4/bt+z8Q0AfvSTH1P90rWbRkucXBmEvCAoW/ugsyn33fF4TPXcyUVNbte1rfmzL0MF/mwBebb5nM91fZfXKQeTO1S/cGT9dHOH+24S8GceRleM9uxGj9qmBR+jPuXxez20+s4WH/uFHa4fjexaZSc8nz2a8gN9HNm8CgDPPvuq0S7t8Xz75fXPUn19bYPqRWv9Yf+AH9An+9zXw9Tml0dPeZ0cZvwZ4dRunZ5d7ySzcfangVsjMy3i56gh5+ijsfme85/k86iceFAVdu1qp87e3rK1EQCkEU+YG6nVf+FnXqa2UcL3JUnts8cxXw/nKoam5M/eknthU/G6a1Hx8z+f8hr+1o9+aLSukzMWHb6udWHHLgs+RpU4PuL4Q9PYvWmdmnMZrjzHa+R3/2+3jfZ5J3asvczrx6FTgh4Wdi3WQ553ti7w2v7wrbeMNmi4h124yZ/xcJ3XsRWpVaKG++iTO7eo/vab3zZaecZj7JYzdhDxdxpRaONs9xLfg9lDex8B/HMUdmxMjiOej8qcj9GORkYLnPciW597keozJw8MSrvHO7d5LbMMR2/9LtX7Q7s+ofPu6Uf3R1R/EHBfTze2rdbnNclab4Pqg8zqvQGfX7bGdTTcT3/n6+9a05LHr6f371P98IGNKwunnn59k/vA5QtbVH+ntHH6vU//RWp7dZPn8vG79twCwI/+yPrDczfsvQMAtq9cp/p6ZGupkJwVADh1Qn3jXH6PSY4/vfMeH2QJKidu5OTumM/5PTNquX4Wr1P9JLJ5p4m4j7Zn9h4MAH2yPtOCz+PwyHnBEHC9Q+qGuphT28x5D5Z2rW8cTPj8NnZ5Tpw6d7qjffu+q9flZysc8vcfScBr+4S8Z7p2ld//8obXQC2pt4MTXmscPNynepjyWDZfkHGW+HML/aWGEEIIIYQQQgghhBBCCCFWAn3UEEIIIYQQQgghhBBCCCHESqCPGkIIIYQQQgghhBBCCCGEWAn0UUMIIYQQQgghhBBCCCGEECuBPmoIIYQQQgghhBBCCCGEEGIlsC3hHYrCdlAHgIB0RR/Pc2qbdXg39yjm31Zq1EYbDG0HegDY2uxRPQ6HRisqOy4A3L5/l+ovvvI61TPyTWiQ2PUAgIaqQE3+ZTLhXeXn4xHVe876oans79X82c9G/DevXLpE9TS0z3n3zm1qW1+6SPWAaU1JbbsJd9Xb79+hOsg4Z6MjbrsEUdRSvdsj52IypbZXrl2g+mc/9xzV9y5tG63TSaltEHLPK0t7RuOKr3En6FC9qvhevf/uB/b38gW1TQPuu1eu3zTabDGntvWGPeMAMGqt/wPAbGjjUD3pU9vp8SHVcTqjci+x4zyZ8XV6fHxC9bhjY1zWHVDb1ongUcSfvdu1z37ylK/rMgQNzxfP7W0a7cnRiNoeHT2h+pOn/DfZs1UtP5/T6ZjqaWijUpIl1LaN+eLffPFVqhcz60uPHvCxm5zvyeTQrkkUTKjtMzvcp3/0gPvd46n10zTk6/fG8y9S/cLOLtVZtA8ClgH8HJXP7JkbHfHzOZ3x/W2dmIXGPmdZOrZL0NCsB4D4aRvydUj7PN61AY/1dx69Y7TukMf0tf4W1TsgZ6vvxKSc1xKdmPtSO7N5sdPldVTd5zXk7pn1g4sXuf9XI35u27U1qhdkb+6N+N6EZzzuVgvHHwObt1tSUwPAaMzHuPX0ntGcIRA5Pp06Ma6/adeknPFcvixePIhJnI2cB2zBfaz4GGc5JDkAAOYVz2kZWbuQ5CIAWJC6CwBOT0ZUD0gsrGruv/Ocz29R2FwynXM/nY55XbMgsfcjnYw95c84LvmZmcz52M/uWd+73uPr+s6b7/H5rV83WhPyWrlp+Pxa5/IWhrZ2Zb66LGXDz1tzcmy0Qcl94OazO1Q/u2fHAIDOvh3nccbv3vcL7kvbJJ52Hf9fHD+kevPk/OtZOb4bOr7O0uXatq1PASCZ83U9fsz1k5mNIbNT7kj1F79I9XsP+ZqcDm28uTy4Qm03nDzffO9NowVO/TDs8rw4jXkMHt9732j93Kl7luDCdX4/np0eGG3o1Cqf/+LzVL/z1i2qT0mMTRKeOyc1z9fZjNR6p9xHi8fO3Szk+Syr7NhxwPepHPB9TZ+xa9KvuG/0Qj6/xomDx+QVyIcHfJ3uvM/34Idf+6dUHy7svu8Vp9T26MnbVD8I7LzvO1vQc/zvxgWeXxZTe0/7wZtf54MvQdBx7pTk7rO5wePdYsL35K2H9i4BAFs79p3UVoe/2xmmXGfnKHKepYr4+94Pj/ndIyTv6ToxP0N7AY8Vl9bteRl0+V4//IC/Sx52+bnYXls32u2jfWqbO++Hn9mztQ4AvPb8a0a7nPJ4vD/h7xImU3sIspDXYmvr/A65fYHXICitr4Ve0XUO9JcaQgghhBBCCCGEEEIIIYRYCfRRQwghhBBCCCGEEEIIIYQQK4E+agghhBBCCCGEEEIIIYQQYiXQRw0hhBBCCCGEEEIIIYQQQqwE+qghhBBCCCGEEEIIIYQQQoiVgLdiJ9SN0408tENM84qalocnVO9lEdWLsjba6eiU2t69Y20BYGvLdpUvypzajk74/E5GXN/bst3ci4bPw2O2sF3lHz56QG2np0dU76Rdqhdxa7XFgtrWRUH1a1euUL2fdYw2OzujtrnzmxcuXDDapUuXqO3jpwdU37/zlOqj432jzWcTarsMZTmlemOXHt/57jeo7f6TLarXJd/vtbWh0aKYn6Hc8fXJ1K4FmzMALBbcN2ZT/uzXLu8a7XOfeoXadjsB13v2HO3s2vMGAG3Dv83ev/+I6m/94D2jPTk4prY9Z+zMOXP9q5eNFg42qG1Q9agOdozIfgFAEPFY2+nw0J7VmdGePnnM57EE8wXPF10bNnD14ja1Tef82U7PnLVorf284P5/e/8Dqk8X1qdfb/g8Xn/jZ6kedjapfu25N4zWkjkDwKP7d6leJtbv4tTuKQBs73AfeLW/RvX5O7eN9nRcUtsL125S/bOf+yLV29YGlzzne1PXPIdOz0ZGm4z5uc3zGde9/Fdbf62ceSwDWwdPDwIeG9NsQPW65nG6hX3mB49/Qm23htepvjG0eXmtw/NWEXO/47MDIuZiCY+7f/zumOphYQd54wqPQZsdvgfdXVsrAsABiUOnjh8NvbjbTaiez2y8Kaf8XGwlKdWfe9HuWbPGfaee8/NckVobABYk9lVHI2q7LFHE97yq7BzqyjmbvAxCEJ7/3NVODR85ft3r2Hps/5j76a/9Fq8BS5J3AKCF9ZuxE5NL54pWNtY+jHn94tVSrRMLO8Qnk9DJL+SuCABlx7kfXLQx52bC53H2mK/fJLYFxzzkThIGPBfXLT9LgNW93LUMZwfcl9LehtHCi9eobdHltfO8x5/t/Z79zT8sR3yMx9zxfnZw1WhfGvDctTHge5INScEIYEzywz65SwPA+u4e1Qfk3AZr/PfASzp0X32B/yYpdHsRr/ffdt7C/PGIvweod+zY7014vfNXrr5I9SsvEj+peIaunKDazfjET4M7dn4HP/07xtXP/zzVJ48/tNrxIbV95jm+Pi9OeRz88V37HHHHiaUhz/llYGNEDX6GgpDn/Cjiflqkdq/mAR8DPXtPB4AOyZVByX3jvnO/CkjOBoCgb5/z/pG9dwBA0vI1SZ99nerlh28arQ75Ps6cO1NO6oHjhseVzoCPsbbF32H1p/ae2338038ntfPiM1xn9UvD4//xY/v+DACCgPvBcMMGyPiMv7NLSE4GgCiy+50m3PbOE75uf0zusADQhNYfLwx4Tbjt5Jz1I/vu2Xk1gNi5235hh9+br162OerAqVn3n/L3WlvOu9PNgd2bBblLA0D8iOtXN+0Y12/wd8OnQ34uFj2eR9La1qeR9zLyHOgvNYQQQgghhBBCCCGEEEIIsRLoo4YQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCc7dKHyx4I1PQJohdfp9alo7TTIPR7zBdJTYxlpeH+71Ddt0GuANbOqWNxvvJLzBycO7vGlrHNnGJ8OhbT4GAM6j4+jYNoO+e/cetS2mfJ0yp8lUUdofHY1G1LYqecebbsabYA16do/nOW8idPkab163vWebKp+d8b0JnOZ++WxE9enY6sGfv/eMy5MHvJnb1WdeM9qwzxsQPXz6hOpH/4Y3QR/27LnoEQ0A4DSbLUvbnCdwmmo1TqPE+Zw3pxuPbBOn2GmSt7e9QfW1HmmInHD/73b4ue06DaleeeF5o/U3ePOlZsFDZBTxGBez2Oc0QA350UJImsNVFXfeKOHz66R8zxIyzsFD7n/LcArenC6vbKxaz3jcOF3wBpn3jrkfBCxEBHztIyeefPj4odGu3bRnGQBip/FpW/FcmXZtI+L7jn/98IifrVeufcpoNy7x+BqG3Ae8ZquXn/2O0b75+9+ktk+e8kZyx6MTqq8P7bMvct6Ar3Diynxmc8PjJ/ep7eERn99izpvSPn5kxwmdZtX/+H/0P6T6eehH/BzPSWETOA0645THnrDkzXLL0q7n2Yzv03zC1+f0zNrvbXO/u7jJm9JOY/48rKYbV7wR4KSzQfXHT21MeHzIm8inHb6vf+NTr1L9pLLnqD3jMWgr4/MOWt40tJvYeNhxGo9uO7Vl3T5ntAen3P/zntN4lMTlj7DPHg6cWmNJcqd+bImDNF5B7TRXDKPzN3uOnYbg3uVjPrXn7tGcP0vT2eJ6w5trZ5mtYRq3buDzDhs7lzBy6honL7ZOI/mIjNNPuK/Ppzzes/kBQPHE1tZPiAYA7YzPe7Zmz8wk5b4eRXweQcvXKoltHPFy7jLce5/fP8vLttHv1X/4H1Pb+oKz3xu8Vnnz+7YmfHCPN369tGkb4ALAWz17Fi9c57ZfIo1SAWDScfxuavUL125Q2+NXeT1Rffe7RgucBqVrEb9jtE7cnJ3aPFA7jejfOeRNtI9PR1S/Rpoc35vze/OPv/8Nqm+Q+/twyv0/dvx/tM5j1ht/7StG21l3GrAvQRDzte8QvW55XdOr+TN/+bWXqH7nnq0Tx06T9s6mE+tJjqpJHgKALHDOrROPQ9h7fRM7DctbHjMnJM9F5F0XAIxT7gOhc/+MYPdhmo+obe28M9h6+Q2qB2SPj2s+dhbyOFSSl0R5364pAFx+6bNUv3Kdx6GW1Anfepu/41mGoD+gemfN7lU+4/H/2jqv4VFy+/DQ3g+qY75uBXm/AQCTyNo/rvj5/M69O1QfjflZBDkCG9d5Y+3dL3+Bz4+8I60LPr++c56f5Pzc7pCXQa++8SU+xiPeDP0n9AUIcLQgcxxyH5lv8DvG0YH102fXeSG65tybGzh1XmXnHTgN7M+D/lJDCCGEEEIIIYQQQgghhBArgT5qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrAT6qCGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLESxOc1zOcLqqc927m8bVtqm0T85yYF7wh/8Nh2XA+ijI8x42MEqe24Pl/YLvYA8Pj+Q6ofPtmn+pPDA6O99tpr1LYFX5N79+4Zbf/JfT5Gw7vHNxXvFJ+Xdk0Onj6htp0kofrhEX/2h/cfGC3L+N6cnE6ofja5bbTR6ZjaTiZnVC/LGdXb2q5V8Al8w/vRD9+i+s/+/C8Y7Ss//2Vq+8H9D6le5Lnzq3a/O90utYxjfuYSst9p2qG2acL3tSxLqjdNY7T9E/4skwXf7zC0+sFTfj47CT9bv/BVvt69tYHRZvd4/Igjvq69wZDqYWjncuniJrXdWO9T/d59G1fKlp9PODE1iviaLI5OjXb7/ff52Euwu2Z9AAAmM+szp2Me15KWx2nHpbEg8S6vuI9Goc1bANDAzrs34PuUdVKqjxc8Vn3jO39ktD948zvUdnPAfea5Z1832qW9S9Q2Cnm8C5xnv3jxJaOlnT1q+y9//dep/m++/ptU/zu/+neNVuR83/M5j+mHBzYXffD+B9T24IjnOXY+AeDC7kWjXb58jdouQ1hznw4bGx/Xe3yus5zHgnnF43fc2PhdF7yeK5o51Q9ObeydF3yfUvBnXN/cpXqbWH9czHj8yNa4r1+5YYPC7NTGOgA4OH5E9U7If/PGhXWjTU/5s19c71E9IM8IAE1u41PtnIsnj3ld2Cmtn3zh2eep7Tc/eI/PAzxOhgGpEzIe95Ym4nUsQru3ScBt65bXGUHA8zudhhMjmpYnnnxhx25JHgGANOFrN184OZCsSdvw+dXOvasldW/u1JZt7ayT85ssw/S7/AwMGx5zNtb42egXx3Z+DffTjYtX+PwmZG9yPo+kw31qUfC1akj9G8c8Bi/DdJ/XveHpyGgf/tGfUNtuwGuBo5zv9+HTQ6O90bNxEAB2C+7r36tPjHbrDr8LPv+n36f61Kn16sEzRhvuXKa2F/dsbgeAn/3KXzCa945i+qG97wLA0Y/eovqQ3HmDkJ+Lg2N+xx5UfG/SR/adwWyTx5VbUx5XXr2+ZbT5kM+v2uT6YO8C1Ts37D5c39ymtsvwra9/jeoXSC5LnTM/cd4Frd14geovrNl8+MMzvsZBy+NaEFs9jPgZiis+v4MPblF9sLZmtP7Fq9Q2ct5JgdVBzquT0rmjVa2Ty1s2NvfdvORjjJxp7734aaN98MNvUttb3+fxhh3/tMPfAfzer/8a1dd3+N1ts2fHKc8eU9tl6MbOvY/U61nD96+d8TjdJXdsADh7YGvqgwP7/hYAFgHfwOEL9r417PC798ZtnhNjx++y7Q2jvf6Xv0RtLz57g+ptY9e1Cvj6RU4NP7/P76W3jm2t85nL/Ny+9jk+79Oax5BZbeNhmTj3q00e99Kn1qdLJzbtOOc5cXztOCXnIuCx8zzoLzWEEEIIIYQQQgghhBBCCLES6KOGEEIIIYQQQgghhBBCCCFWAn3UEEIIIYQQQgghhBBCCCHESqCPGkIIIYQQQgghhBBCCCGEWAn0UUMIIYQQQgghhBBCCCGEECtBfF7D2WRK9bPTM6MNBmNqm0T8G0pRLKheV7aDfBLzzuqLgndWD8/svPMF/71HsxnVt7bWqX7r3beNNpva9QCATtah+tOnT4xWzk+pbRTyjvXjKZ83e8yt9TVqu72zTfWjwyOqv/n9N43WNAGfx4d3qL62vmO0zmCL2s7nfOww5C4cp9ZP2raltstw633rAwDw3q0fGe3Zlz5FbQebm1Q/GXM/bVq7FkHA16co7BkCgDC0ZzGMImobgI8dOevZ6VhfHwwG1LZ1vqs2rfX19R0+j4OH71P9bMTP4nRRGm00oqbY3ODntqq5/Qfv/8SOsfMZatu2fL3PxvbMBTFfvyBLqJ4EfF3v3LtltPc/tHNeltKJsfP53GiTOY9rO90u1cN2QvWAnQsnJkUxzxdBZfXA8fO7j+9R/Rvf+w7Vt3t9o/2dn/sKtR10+X5vDG38rslZAYA45r7hxQogM8oLn/oqtbz+o/eo/vXf+TWqP/fcc0bb2bpAbR/e5ef53Xd/YLT9p3wPLl28RvU33vg5qne7dm9GZzz3LcO44vkqKGxASWDjFAAMIj5Gb8DzyCK0Pn1Wc5+pGv6bYWTnN8sPqe3tR/xs7eb83F7ae9ZovS6vuZiPAsAss/m+yuyeAsBze5eofnrG1wSJjWUv3LxMTXvga3L/wYdUL0a2Pt3d5rXY9k6P6p+6+pLRXq74szyd8Nr87Sd3qR6T3F964WNJmsgZmMT1usy5rTe3gJ+ZtrF+XRU83qexE2dTG2eDktddUX5C9W7D83WXnNPJKR8j7nN/H67bs9S2PDcsFvzslgWPC2Vp9VPi0wDQD/meXb2yR/UrOy/YMTqfpbbHNX+ete/82Gg9pzaqybMAQO3cT8PU+lTc8JplGe6+x+8Y6cjGmsNHD6ht3uex48Dx0/nAxtO9nV1qu3HA93VjbMfofOUL1PbH93jcPH7ykOrND+39sz1+TG2DE+6P+WN7916Qdw4AcHrC4+ZwwAPO8TWbH44f8XP7gwdcf/4yX+8bV22ef/D4EbXNS35ROfycPVtXP/cytV1b476zlXH9mMTaoubrugwPPuA16PCiXbf5qb13AEB7aOMDAJROvs4wNFoP9j0GADRw7tMBeTcR8NgTt/xsra/xWB9s2lhfJDz3lc69geXEBPy9WwJnjIDn0BL2Pk1+DgAQe3HaeY0z6dt3R+s/99f4GAc8rqCx671F7m0AMD3h9ezpKX9/d3ffxrisy2v2ZeiE/Kx1Y7v23ZDnzbPDY6pPnuxT/Yjkos4LV6jt3nV+N6vIO7v97/+Q2l5a53n2ccrP3HM/84bRNq7w++ei4PuakneNnT6fx9zx0fAqr+1nJI0cHx1Q2+2E10tnC/6+q+raM9o2fIKL2qlDt+z7iI7jO6j42Jshv7uNSRya48+fL/SXGkIIIYQQQgghhBBCCCGEWAn0UUMIIYQQQgghhBBCCCGEECuBPmoIIYQQQgghhBBCCCGEEGIl0EcNIYQQQgghhBBCCCGEEEKsBPqoIYQQQgghhBBCCCGEEEKIlcC2c3c4PDmm+tOHD40WOp3fo9B2OQeATsd2vQeAprUDzWczapsXJdU3NreMtrk+pLaejhMuVycjoz158pTarq2tUb1t7JpkCd+Wqsz59E74BMdn1v76M89R2ydPjqh+eHBA9STpGY1sFwAgTfm3s/WNDaNlvQG17XY7VK8KvladoV3vfFHwCS7BeMzX7Rvf+DdGe/G116nt3u4Vqp+c3af6YsH9gBHHGdWryvpd5axPEARUj+KI6v2+/c045vs3z/m5LWurrW3uUdssTaj+1tt3qD6ezO0YgwvcdlZRHRGfd2+wYbQP7z6itgeHIz503DVaEPK1TiN+ttrSPiMAvPW9bxvt6dMH1HYZZgVftwA2SNQtX8t5w/d1M+H5oprbZ85LPnYQEAcDsL1jz2LRsfsBAH/wp9+i+k7Gz9ylTZtfnj66R20LJw4O12w+S/rctmycgMyPM6rCnv+m4mO88bkvUf3tD96m+h///u8Y7dVXXqK2/6+vf43qt2/fMdqNq5eo7TM3X6H6ZDKh+mJh9QfO3ixFwM9rRf7/kvGCn/my5jmvrXlt1O/YXL1D8iMA5OQMAcBsNrLzy8fUtqitLQA83OdjHx7ZGuPSpRepbZzxGq0JbH6Je5vUtmx5HfrW0YLqLXn27eGU2l769GWqf/n5a1TPSE0cJTxXLpyzeOeBrcFv3f6Q2v7qV3+W6rf/nzwHTEkMaZycsyxhwJ8vqG0Mjx1fr5wiNEjtGQCATmbXOnNqpvUu95srG3aMesbP6KDP/ffwiNfwSWvP2Pouf8ady/xMb1/YNdra2jq1hVPrNU4uaRqbRx894PVOGvP1u3yRz3tve9tonS63nT/i99NPffqG0VrwuFrnvGbJCz7vh4c2bt17ymPiMvz6P/9XVF80NgYtjvl95OQxvzMkDT/LaWj1yRZft9OSx80HRyOjPfrGn1LbKblLA8Ci5TkjempjXtLrU9th5dTIJOfHoVcc8fP8iP8kHo1t7innzv0z4/XsNOP17/5Ney6SmPtu4sTDe+Renz6xtSUA3Oxdp3oe8XORkp8MAucetQQ9XsLj6MjmhnjO/T8D991Zzv1u0djznQx5vgide0qcWaepwefXkPdDAJBd4u8MSlI7lE7N6QwNBNbXg8Z5BxDwmNDETh4he9aAb2QaO7k84vaL2p6veMPmPgDYfvZzVN+9sGO0vQv8vcNanx/+tuI5oCV30XrO65hliFNeP6aR9cfy+JTaHt9+TPWDM27ff9bGiL2Xn6W2izPnDnbrrtGeW+N12+kL/N632ef1wcazV40Wu+9Z+Z40sDGMuBwAIHHySOXE+mjX5ounD3hN88yEP+NOjz/PUWTrhKjl8fjEuf9NyTu2bofPI5nw89mv+LPfJO//D51vBedBf6khhBBCCCGEEEIIIYQQQoiVQB81hBBCCCGEEEIIIYQQQgixEuijhhBCCCGEEEIIIYQQQgghVgJ91BBCCCGEEEIIIYQQQgghxEpw7kbhvT5v2LK7a5voPLhjG74AQF3xpkxRxBuObG7Y5ildpwnr6Zg3n+kPbGO+JOVjLJymxdMpbxyzII1VM2d+hwe86Quz31rnDS5PRrxp9+kpbzJYV7aJ03zxAbUdew1Uc97AKo5sM6KO08y7yHk3nYY07Cwr/nut08CwJc2PACDr28Y2addpnPsJ8KMff89o3/zmH1Lb117/CtVb0swbANLQHtuQNPYDgMRpol1XtlHQouHNg4rCaUxe899cTGyjrJg0iAb8Zmqo7FmcTvj8Mqdh0XCPN/Ka4YnRTie8AVblNEhrneZrXdKcNu7wc1GnfH5JZGNC5nx+7jkNy99/n5/zb//xHxitqZ2Oe0uQ506Tt9DqrdP47eiUx6T1mPs0a1Acdngju47TKLm3bRvLPX3E1/LiBh9jOuc54PaZbeLJmo8BQEma4wLADvHTrMP9qI6chtLOWZyTHHB8dEhtN7Zss0oA+Nu/8veofuf2+0b7w2//NrW9e/8W1UdT28Rs/gGPTccnvJHzOqkpAKDXtXvWup0U//w0pZPfyBkowX+/CnnMnLX8XIxI/ZI6jTs7HR5oLu7ZBtivJE7j2DFvVvvoeJ/qs9rO5c7d71Lb0RmPFUVh16Szzs8nEqeJZcDXuyTNHOP7fB5vPeINt7/y2eep/umbtn7uVbxeOnrK4yFr7Hlxm8eEl196meoXd3njxQ9Is9/YqTWWxektCtYv9+Zl3rxz7wK/p/SGjk5ycEzqKwBw3B0xaXQaFE5ObfnaXdjm67+YjoyWk9oNAIqK/+bT+7bp52HMz2jg1NklaXL6kU7qyAX337zk+rvv3edjk7jQOGej8OZH1rss+PpVU97gtZzzczcq7G/uPfclarsMbcDzdZZZn956doPavrTJ9aDmY5+QvP8w5Xmn87LNDQDw6p6tEdaGfB6ba1z3mrmmma2pQ6cJa+LUQWFqfb0T8fMZNXyMBbjfdQcDo8XOnWHu1Lk18X8AiDMbiD7fX6e2SafLddgxWicAR867n5LU8gBQL2we9e6yy9Bz/p/ckxNbIyfOHTZq+LudWYc/85S8MpvXvEa+vMlrzbVrN4x2NuX3z1nhvHvK+bu0pLDrHLHu3IBzCwDa2o4RODHTG6N2YlbT2vPSOnm/cuqMhswPANrS2k/GPKbXcN5RtnZdW6dpcd2xzacBINu6SPWUvOtonZy9DBWc+nZm1/7Oh3wdTub8/nThtZeo3r1ywWiFc7fNnTvlNbLfUYffpZsrPN4NN/m9NCdjh+T9I+A3rm/Jfayt+FrHzh0tCngRGZP6dDbkvnu24PpG1+YcAJiS9695xPcm9WI62cu84TFo5rysGj/lzedfX3vOaEnC77LnQX+pIYQQQgghhBBCCCGEEEKIlUAfNYQQQgghhBBCCCGEEEIIsRLoo4YQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCeLzGg66tjs7APSzjtHyPKe247MR1Sdzbr++aae3eYF3t3/xtV2qb23vGO3C1ia1fXj/HtXf/smPqF4UtiP8bM47wrct7yq/vWWfpy6Oqe3T/SdUz7KI6mEQGO3w6JDaBhF3haqq+dihtS8d2yDk387qurRawdcJ5Fk+gj87YO2j2LNdAmdai/HEaL/3m79JbbcGe1Qf7l6neh1lRosj/mxN1VC9quw61w23LWrr5wDQEP8HgNlsbLTjI2qKbm+N6nFm402SJHyQgPvX2sY61be27XOOJ3a/AKB1xo7iLtVr4r6LijtJ4cTJNrK/GQb8XBThlOq/99v/muqPH9y3one0lmBW8EH7Hftsw4zv66i08QEA5uB6mKRGS/sDapttbnAdhdFOn7xPbd9/e0T1CnYeALC5YXNUBH7m4oSf57uPDox29fJNantx5yLVvQ0/Gdmx7z64S20fH+5TPXRC7NMD63f7Tx9S2+nU7gEAsKWaT2bU9KTlee707JT/5syeo7U+j03L4MXYhmyJY0rXAQCSgPtdG9lYuij5us0nPCZNxmdGy0I+kZ01HncvX7xB9d1B32iHhyfU9nsnP6H6rfesn9YVr2lyJ97VPWf9+rbGhVMv/fh9Ho+/9b1vUX0zteNcvcBr7auXeT2wvmZz0cYan9+H3/0h1Sc538sksc/egNd5y1IVvHauKxvvv/0n71DbYGL9FAAGHZ6vY1JXLqa8Fjgt+fwWpEYoxrw2mk+d87Xg57EurH1R8vVvWRABsMhtPC0qPr+SrDXg3wMqMo5XF6Jx7gFOPCsau95VxeN3lvH6aLqwc1lM+BltC1u3AkADHote+fQXjfbXtp+ntsvwv/hf/S+pzkrCxEnAQch9I3DupS3bFGejIlKvAkCvb89cnPIYy+6qABA5NX9AxqnrjxeXytKei9Crx5w7rAfL894ZcrYAobNnMck9TeOdT36eaR3iTMR7n1PM+Ng5iU956cSEJZg58bi/Zuu2qLB3ZgBoK76vMXmvBQCdltwxHLfb6PM8vnHJ1uXhmP9e6tRpLCcCQDuze9V8zHPRMN/Nnbzg3NFYXgCAjOg1uzQDqJ3zUjv1R9Da81LmfB5VwHNAE9h7Q+nk1cmc31N2d7lfbu7Yd5Fx6rzTAPeH89AvnP0eze3vZ7xOvPKl16ieDLhPl5V95qx23p2QmhcAitbWXXnMfy9x7rahk4sKUlu2Tkzvkrz10X9gz4V3R/N8N0n4s7eNPQPBDr9Hnc24Tw+cM9oj7wanHe533geBLhkjIHMGgInzfqYa8N/cJ/7aTfh7m/Ogv9QQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCfRRQwghhBBCCCGEEEIIIYQQK4E+agghhBBCCCGEEEIIIYQQYiXQRw0hhBBCCCGEEEIIIYQQQqwEXrNzQz6ZUb2zZruUv/q5z1DbNE6pfnx4xO1Ta7+5uUlth4Mh1fu9jtHC2HZyB4CT01Oq5wXv5h4EdpwwDKht2zZUr0rb+T1s+e8NBz2qb2z1qX6wb9e10+F70Drft+raeR6mNUwFgpCPzUZuWrseANC2ztiOfUR+s3WeZSn4tMCe7uG929Tyj//wt6n+F37571J9sLNntDKfU9si5+uTLwprW3PbIOZhInZ8PQkTO7+Cz+9sfMLHKHKjZRn33bLksamp7RgAEIR200pyDgHg6f4h1ecLbn/x0o7R1jN+buOUx6GU+HqEitr+1m/9GtV/93d/i+oU14f//EzKBdWT1PpSFPD4MEz4fi/CLtW7l+3aVyWPpZjw+R2cHBttRs4KAARRRvXRZEL1h0+tL2327VkBADhx7b1b79oxNrapbdpZo/qi4GOPxzb/nZ3y81k1TtyN+V72OvY5m4rnxNyZX5HbMxA4eXU8GVPdy/0tiWWHxwfUdhnKgB+2lsTepuHP5uZCJ73FMfGxxtZFAFB5e0L8ce74wOhoSvWw4OdigxzztXUeM1969Rmqv/HSc0b7wZ/+mNr+9m//G6rXtCIBQOqusMfnl6Rcz2O+3mekJn6wv09t43fuUL2urT8MN3mM3NrhMcFxKbDQXDefQB0FoPTqbLIvbcrr/Xv7D6j+9MFPqH7r1veNNh4569/nsSPr2zvQ/ISfDTRO/In4uQsDUnu1PMZGIa/TaCqJ+IaHfHqIImds4jiBV1A4866ceB8ENke3IY8tCbnnAUCY2LGjmOeGOHJqhYif6fVdm3dnc15XLEPXiTVtbX2G3XsAoHZitaeHxBFa59gvCl4fTQ/tXkURd7DQ0YPI+f8uSU6rnfuLly/pPMDPYePdjZykGyf2vDQkTgM8fv9ZY3O8AO49u9Ub4k/An7GuToiryE82Xm5dgmSLvwta27L3gPHhiNoG4PV37NzZgrG9U/addVt31u1id8Now4DPY17ye3NV8zPXDOxvemfcuxvVpO7M3Vqdj1Eu+N27IvZlPeJjOPd3zx9L8jxNxeNKlDuvPlu7rnnN3wuezp3zPOO+ns/s/WX9Ar+7Aby+OQ+DMX9X8Hg0srYXL1DbasDrRydUIWztOtfO+8DKiQWDjQ2j9ZwzPnaSkXNlAjsCbmRkAQxAXdl1jSP+jiJN+buBwHnX0ZC1yvp87KLhNVAV8t/c7Ni9HFf8LlaQd2MAkCQ2PjklIRqnzmtLPvbTmT1zPVL7nRf9pYYQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCfRRQwghhBBCCCGEEEIIIYQQK8G5G4Xv7z+l+kt7ttFNx2naFpPG2gBw6dLF804DiwVvIPLee7xBYD+zzVMSp5nakwcPqb63s0v1iDQr85sb8eY9s7ltBBXGvBHSzSu8qU8YcPv3b9mmt1G8Tm2fv3mD6m3Ixz4c23lPZ05zqBlvbMN6/YROY7jWazTmNI1iDd9Cp0nPJ4NtitOCN9V683vfonruzPcXf/lvGq3X442lSue8VKVdH6/Bj9cIsKm4vta357/X5Y3XpjPetHExtw2K64qHq7LgTdaClj97lNoGTCVpAvXRPPieRY4/Vps23sR1n9oOQq43pDnc73z9a9T2n/+z/5rq8znfm39fVM5xHc1Js7Se05TMaZA5qfiZn06tL8WO/9dOI69FaXNU2uH5rBNzf5xMeHO/tXXbpDfLvKZaTh4hTS/Hzhk6IY3JAaB0GhsmpDmo13g0Iw3fASBy1qQorT9WudN8E7xO2Nm2tcZgwM/Q6ekR1WungX1AnrO3/udv1ufB9g8AWtbw3G0I7jXddJrQ0aZ6TrPhhPt6UBP7lsfMBlwvEn7mjkkeOXYae4YVPxfrJKa3u7aBMwBcef1Zqp/e4TXk6Okjo5VOo8Iq4/4Y9Hndla3ZRogtacoHAGXDnz0keTsc8nnUrPE0gMbxNdYgt/U6Bi9J7NR++cw2NXxw/y61PTy7R/U64rGwM7DxoA6dbpOR0zC3tI1EWSwFADjxMcycfWnsWWqdbp1pwvUss2c6Tvn8EtLgGAC65B4FAL2u9bPIGSN3mscOHPthx/oZOeYAgLGT0w4nNrawhp8AUMx43h47jUPbzN7HOgOb45dltuDzYjVo5Phd5XR19sZuSENfr5F066xnGpN6wmnEnXj9wL2cRn7z/O3Afbx7Zkju+oCTtwG0bN7OM3q30sYZuyHdcL2SoHWa9TLz1rlvtk6z8drZy4rMr/4EcsbwWZ7H7963efzxoX0XAgDz2RnVq5jXQUljn2OQ8nvK5uMfUf3mLXsW58WM2s5yrhdO7TUmd/VFzuNu7up2jMa5c9Ves3FS7wNASMaJvPjq/CZK/uz0XZDj/3CGDiJ7zvOYJ52my31k3uX2o8zqGxu8LsRf/S+5fg5u3fmQ6uGmvc9sbPAauXXeVwakITgARKTGKBvuG0mf36v6fbv2hVN3OKGRNrkHgCSx7xhCpxarnXk3JMN4d1XWmBzgDcE/srfzLp238wHxIwCYktgEAJcyu8drFX/G44CfW/Z+zDtaaeb4iPPOII/svBcNj3vnQX+pIYQQQgghhBBCCCGEEEKIlUAfNYQQQgghhBBCCCGEEEIIsRLoo4YQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCZz+6paq5l3RY9JwPSy4rdeZPnC6qAeh/eZy9/33qO29u3eoniW2E3s37XDbiC/H2qU9qt94/hkrBrzt/Wwxp/pbb33faNMpt22qjOrDPn+eKEiMtra+SW0vX7tK9W5GNhjAtYmdY91y2/tPHlH93qN9K4Z8D5qWf38LY7u/ABDAOhXzp3+/cN+o6xnVf/zWH1E9i+2zfenLX6W2veE21be3rX46K6nt5NFTqheOT09PT+w8OtYXASBN+X5HgY0VZc5/r1jw9ctnE6pXsD4zL7jvRiH3r5jMDwC2B12jZeDrWp4dUf0P/+A3jPbf/PP/mtqenZ5SnT8NyKn4ZEhLvm7TkMwgtmsGAE1dUT0/HVO9V9qx05SPXbR8/9ra6n2ypwAQgY9x6QKPsWvDvtFOTw+pbRLzWN9Gdl2PJjm17WT8bCUt35tOkhqtnC+obdPwWBbUXC8W9gy0Tkzv9Ow8AODVV18x2vpwi9p+eIfXCW3Dz+LeYM1oW1s8di6F43fU1DnETctPcev9PyqBHaiNvAjBfQOw8bvlxxOxl2YjXqe0JFoFDX/GAvxH90msr5xa7OJrz1J9uLNO9eTtD+zvfXCP2rZnI6pjznNXSfTUqdGahj97NbXPfrDgMTJIX6R6d92JwXRvzu/DH4eq5PeGtrQxqJjxvNc695SO45TPXX3OaEHKz0DpHJk0smejWTiZltgCQJjxeL9Y2Ng+n3FfCp36ttO1567Xt7kIAJKY54xexs8uyxlVy89dteC5ZG+Nz+VC3/qkd3cbFdwnb49sbVgFPL+ETvycBVzf2NoxWpTxsZehcaq2hpzD1skNpZOva0dnvuTF5DDhPhNEdoyW5CIAKCoe21j8AYCysvOOHd/1zgVbP+8dRUTqLgBuQV3Wts4IAj6Gd8cInLswyPmqnFrZ21827yDka+2VLBE5+wDQkLtRU//0bx5/eofn4IODA6MVNfeBCvwZBn2eg29uWf1yymP6VnVG9cUHb1mtmFLb+Zzn8VnBa/7jsc1/ufc+zqnVFyROjz1bxzkWFf9Ndu+qK75+bc3PhRfjKhJDWueeznwUAALyDjBwcrbn/0nm3K9Ijq9TnveXIbzM70TpxtBoc2ef4oqvcezkzpOR9dODY363jefcd4db9j3rweExtX3ovPNxriRIyZ0ydvYvdHJUQPSi4PfJonTirpPPGvLeoXDuL43z3iHv8PvLhbhHNF5znc14vGmIT89z7juTOd8FL8+V7chocfznr6P+u37LK4QQQgghhBBCCCGEEEIIcS70UUMIIYQQQgghhBBCCCGEECuBPmoIIYQQQgghhBBCCCGEEGIl0EcNIYQQQgghhBBCCCGEEEKsBPqoIYQQQgghhBBCCCGEEEKIlSA+r2FdLah+/9Yto3XSjNqGMe9+DqfbPGsUf3p4SE33dradMVojHR/wMdCxXeIBYDafUH3/8KHRmoY/y3yWU/3gqZ1LMeNd74OC670e38bx2dSOEd2ntk+f8me/MLxA9d2qsfMY9qltdP0Zqt9/cGC08Rlf6xaO77R2fwGgaez8osgZ4z9QmnJO9Te//XtG23/yiNp+4cu/SPXXPvNFo3nrE3rr5pzboiyMVlfc/70dSZPE/lzIv8EeHx050+Pzy4ZrRiujlNqud4dU76T8zBXzsdHuH96ltn/we1+n+re/9XtGmzsxyPsq3br/Ys/FJ8Eg5uuJNbv2QWP9BQA6U/7ML918lep/42/8HTuPwTq1fefWu1Q/OxsZ7VOf/hS1jULuvVXF4/RibuPxo0fcN27d+jHX77xvtCyzZwUAApRUn9fcB4rSzjuv+BhJ5PiXFytI7m+cfFbzkI4fvP0Do4UtP+ODTpfq21u7VF9bs/VDFnf4RJagbfjDtSSPMe3f/oszhldHOfrHsSWxt3XicV1zvWq5HgRWDwMeX4OI+3rbVkZrSI0C+P4VbwyovvP6C0arvPi/f0L12RHXizNbA7UFz/tNw89iW1s9d2qH/R/z/b306k2qJ+u2pqtdv1yOyZjH+6a0tcOFi5ep7d5lfr631/jelnM79tHpiNoual7DdEhsi500Gyf8btSG3K87ic2jJamvAF7zAkBIzqlX0zFbAAidLc/I/Bo4cb2cUX0Qcp/sxXYugw6/YzQFn/cGbP2We7HFecYumQcAJJndy9mMP+MyFJWNbQBQFyQeePnF8Q0v3EeRXaMk5saRUwuwdOT5aByf+1WEi3d/qWvuj4yE+DPgz5u9owD43YOtKQCEIdcrp/ZitX0UO3mx4v5QkTVpSu5n7rMX3L4NSK33Cfz/s/2Lr1C9t/uS/X239uAbGDvvsHqp9Y+jyRm1fTBxzmJp51LXvNZs64/3zqIdWF+qnILHq4Nq8g9Fw89Q49WiTkwPmX3Lxw6dpBN4v0nmmDr7Gzk1TEjudAnZcwBIuzwXxV1ea6SsdnXuisvQ392k+tnE1ldMA4DayTlxwePMfGFj1ZzUbQDQyZ1cNLS+28z5e+fJyL5nAYBFxM9tPT22v+fExiSw7ygAfh/Lunw9yorXaLnzHixLyThOPksy7o8HYx6HfvLBbaP9zHPPU9uXnfdd707se+qZcz7DjMcy706XEF8LljgX+ksNIYQQQgghhBBCCCGEEEKsBPqoIYQQQgghhBBCCCGEEEKIlUAfNYQQQgghhBBCCCGEEEIIsRLoo4YQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKwNuREx7evUf1k0dPjJZFvCN8G/CxG0cnzeYdEbj6zDWql63trD6enPIfrEsq37/Pf/O9Ox8aLZ85Q+d2HgBwdmq7yldVQW0fPbhP9ayTUp2N88WXrlPb6Owdqn/zO9+m+vXsgtEu3eBjlxd3qD7odYz25PE+tQ0j/oxNy7/LNU1D9f9/oGlqo929zffv+PiY6g/uW9995fXPUduNwQbV226f6sUws7bO4S/mC6pfv3LZaL1+j9p+97t/SvWy4OdoZ33daNvb1p8BoBNHVK/LKdXf/JM/tPP7zjeo7Z3b71EdOL/v+pZOUP33xC/9rf+Y6ldvPG+0wwN+5pOAn+2XX3mN6s+98LLRityeFQB47qU3qN60dkWThKdJJxWhKHgeYf746utforbPv/A+1c/+xT8x2v3v8xhd1vzZg4Sva13ZHBUG3I8CRy/JGACQkGOUZXwBw4DXD92uzRdnZzyXHxyOqH54/JTq4zObo569yvPZMjD/AkCdqXUczPM7j4DEAm///DEYTu4NnfPiPHoN6zNxwOOuOzapOcOa/2AQOGVvyH037Nl8tvXiDWpbX71I9dnTA6qPH58YbXp4Rm2rOS8ug9g6RFDzMzQ5PqL60V1uv/uqjdcIP5ncUlQ8bjaVjWOVc3UJA14njubcnwbZttG2L/NaIK9yqndC62dRxeuawKmDZjn31YjkwMDJR96Z7nRs3Ew71qcBII752F4uzhK73knm5JdyTvVgzmuplNzHEmffo4o/e5JZ+8o5/7Hj1w3ZXwAAWZM85zXnMnjxPgjt78ex8/8pOrHQy+9ZaveVnUMAiCL+my1Zz9qpSULyLICfA9lz1jW3rSr+7DW5I6bEXz6ydTKgM+84suM0Tr705ldV3vPYPOW9M6gbJ6aStfLuzJ7/ufMm49Tey58luLD3AtVbUmh4z1Y46zYpeKzfJ8PkHZtDAKByfAMkfvOsBfCsDHSccx4lrA7iG1g2XA/IEQ2d3Oz5fxTxfAvykyyOucYAgobXaW1t9ch5p+fplROfGCH4M8aOryWkzq1KPo9lKOfOXpF3Zb0Of4cDJy+kAa8bttOunYdT8LdzfrbQWTPS3qUNPr05P7dzx08n+WOjJYGdMwBk0YDqTWPrl07GT2hZ8vqvyHkN1Mnsu6265VEh7vI9iJxzNDuzv/nOhL8X3B5yf0hJnYzFmNo6ZR5Kcj4BICdrsrZmfeG86C81hBBCCCGEEEIIIYQQQgixEuijhhBCCCGEEEIIIYQQQgghVgJ91BBCCCGEEEIIIYQQQgghxEqgjxpCCCGE/PPEbgAA/elJREFUEEIIIYQQQgghhFgJzt0o/No127gXALLUNi1hze0AoHSa83iEoW3EEzsNiMLIaeRFetXsXeSNAPuk2Q0AdEiDUgDos0ZxE94cJ5/y5o890pANTuM1pEMqhxFvoBzWtpnONaeR5QvXeWOW3XXeIP7ObdvgMu/w/V0f8oY3n/vsq0bb2OTzeOdd29gaAOZOg8WYNHHymtT9h8rH6wfLrcenvDHun/zhvzba+z/hDbeffd7uEwA89+IrVN+7/IzRuk6zce+r6va6bVjkNRR79SX7ewBQOM3eAuIy9RlvVv2jD9+l+tvvvEX1+w+sn7JGSJ88/936+n/0D/4HVGeNRYuSn+HGa2Tn9BwsCtuIqnG7bHK5JGNMpryRaePEE+/ctrSBHH/2S5d5k+q/+9f/ntHWOjz+75/aGA0Aj57ep/qTgydGq5098Jp+eY32UtKcPGa5D8DlPZ6jXn/OxptHTx5R23fef4fq4wVvtjwaj4x2/8lPv7ll7Tb/Jn7g+a7n0s50Q9awlTQNBIDG8Uc0Np86vbwBpymot5oNaWpdOs0WG7dkZQ37uI/GIfc7p8c06Mw7/GnqhDfxS7r8jPZ3LxltdszP7eT4kOqzkbWvSBwDgCbizjNfOI1mSZPRwGtGvCSsoTUA5MSxW6fZs9f42Gs4PMtJzVp7dwzeFDJo7Vp3U2476PJGjHXDna8hxUru1DU1aSAMOPeohD9j5DUIJQ1oASDL7Lwjxz9SR0+csTEljSid5sleg+eYNEp2+pcid5oqe022g9DOJXFy8TLkuVPLNVZ3jgVa5xm8oNy0bN2cdaideEB8ycsBrROr3RqQNul1Gr233NcbkqcWzn3SS65ej2PWhPzjNuJm5xYAItKEHI6tF8taEldar6G6o7fOOxc2duMduiWIYqepO6lBA8c34pjH3T5bYwApGafrvO+qnD1pST2WOkWd1yg8dd4RhbH9zco5+4WjVyTc1LWTb535eQ3EeR7+eI3CvattReJh7JzbJHQCJbmLenfIsOUTCQJ+3w/Ib0ZkzsvSS3mN0c1IPCb+AgCxU79UpbMWpDaqnLtEPeDPPCGO50WNrOe8k3XO+dU+eefb8jHmC+fZSY0WOwlt2Nmk+sBp8j2Z2Mbio4lzf+nwMeDE47pj32ufTLiP5s49IMzsGBvOu/iG1EWAHxPywvprRN4XnBf9pYYQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCfRRQwghhBBCCCGEEEIIIYQQK4E+agghhBBCCCGEEEIIIYQQYiUI2rblrcqFEEIIIYQQQgghhBBCCCH+A0J/qSGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLES6KOGEEIIIYQQQgghhBBCCCFWAn3UEEIIIYQQQgghhBBCCCHESqCPGkIIIYQQQgghhBBCCCGEWAn0UUMIIYQQQgghhBBCCCGEECuBPmoIIYQQQgghhBBCCCGEEGIl0EcNIYQQQgghhBBCCCGEEEKsBPqoIYQQQgghhBBCCCGEEEKIlUAfNYQQQgghhBBCCCGEEEIIsRLoo4YQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCeLzGv6v/8v/M9Wjimgh/1bS7fSovra2TvUkTY0WhAG1DQOuN63VWiYCaOGNUVMdrR3HtXXGBpmK84iAOzZf7zC2g3fShNrGzuctZ1lRV3YuRV5Q2/ks5/rc2p+Nz6jtZDqm+mwxp3pRWscs44ba/p/+9/8bqp+Hf/Jf/BLV1wr7zMNBn9pu7WxSPet3qB5F9ti2iKhtW/INXBQLo00Lfi6awJ5DAIg7a1TvbNjz3N+7RG37W3t8DBITooTPAxH36SDk9iEZJ3D8v235uiLkoXMxOjXam7/2T6jt//G/+mdUf3xifT1q+D5mEZ/fMOPz66b2QfOUj/HffP821c/DD976NtXr0v5WyY8l0pT7fxzxtahJsGqddWsC/sxNY+NanfP4VS5mVF84ekT2ao2cFQAY9HmsYPG4IGcZAJqGL2zg5OecxOP5jMddNCUfO/ISidW9fOtRk71ZFDznVBXXm9KxJ/F6nPN8+w//s3/kTfHfyV/4z79A9SCxv5V2+BlOnfOat3y/mwXR+fYh9eJdzHS+13XuzMM56AEpPkKnCApIzQUADcntgbNOMakrASDiQ6Os7GJVTmEUObmorfjgZWjnXSdOzHLWryXrHThjoJdROXHySESOwOCQj/G1/8PX+G+ek3/8v+X/fQm7plHCfS/rOjWJE08bUoBPnBp0NuN6RXJMUTt76MTkpnFq+NA+u3fXiRO+h2ls/T2Onasf8cePcA4Hs2z5szQ1uSwCqGseZxty1r2xS3IfAXiOqVrnbHhn2ru6kT0bplNq+3//n/9VPsg52D/hY8bkzHp3b69m+jh4MdmF7p9z93bdi/8D2yrvrho4dQar+b2z5eL+qNW93PUxyyD+H7jrev5z+3F+7uOOXdfcttvhueQ8vPvmH/B/aGyNlxf8PUET8LhROLn2zpMnRvu9b3yL2v7ab/wR1Y+fHhqNvU8B4K597Pjdxqa9T3z2jdep7dVrV/n89veN9pMfv01t7z+ytgCQO3U5SOz13L9x6tmPE0Oi0KkBnXcJ3Z59R7m1vU1tN3f5u4v+2gbVA1Ibhil/xt/8p/+U6udhsMFrIHa3DZz4Gjm7Ejl5JCTjeLloo8vX/tVrF4z23Dp/Z1wv+J03KPk5Ssg7M+8ZA3j3FKvzisa/iy0KfvFqGjtS6pzxPr2L+feX07FdqwOiAcDcWT/2zrzjXPUTp05wrpyYkaXKnWf8+ocnzij/X/SXGkIIIYQQQgghhBBCCCGEWAn0UUMIIYQQQgghhBBCCCGEECuBPmoIIYQQQgghhBBCCCGEEGIl0EcNIYQQQgghhBBCCCGEEEKsBOduFL44481PQRrfRU6jkJY0QwGAtMObKzYgjW2crkKh0/yNtWtpnEbhHl5zP9bEjDW3A/zmRiFpVhN5zVadpj5e/7aALJbTkw9wGup6Y7Pn8RrQhk4XctbDKQidxkXO2GnWpXoQW1+LnOY9y/DilR2qd0hTxF6fz7V7gY/h7Xc1t01+qhlvw1M5Dc8q0hTYW57WdTCvuZmdtzdE5PgG9YOWx4/Wa74E3hyurW1zutBpTNR6j+g1IQ/sHLMOb3aVeU1lSSyLnVAdOY0zQ2feGWkon3z8ToX/Tn7rd3+f6l//ja8bbTyeUNsk5k0EvQbKrImoF+rjjO9JnNrfXOtzW69ZdlvyJlzs/LtN6LZ4s7deap99uuB+Xtf8QHs9Q4uFbZZ9djaitovcaU5e8zUJia8nHd4IPnKa+LFOgHXlNJ8lTfE+GsKJhySGDIabfB5LNApvu04DRZYLM362vXjMmjoDAO1Z7PQD95rosvrKa8AaJTyuebVbnFn7OOMTZA31AKBmtaIT0xOnUbiXoxYT6xsFaR4OAJHX2NapX1oSyrwm2IGjo2Mn3pLGiAAQZ06TdBJXAID9ZOPWp8uxffEi1YOANER2Gl0nKfc9ODGlIPEjz7ltsHD8huw5a0wJAE5IRuPU3xHx4Zh1OAYQemtCxgidBqpBwCfo9PnljUad+cWO31ROkqaNwvk03CaZNVnX4GPc54A/ozEtCUU5eEP6ZZjNeawJSJtSr57OnCby3l09IHro3e+88pHtn7PX3r62TtNW5ktek2+vwTmT2Z35o3/w5I/XdP5j8TGG+Gn0A/en4fjIx3nEn0Kj+v82CblrAUDLdCctRJ0h1Z1wjJeHA6NNx2Nq+81vv0f102Nr34DXheQqDQCII36edy5cMtov/pVfobbPP/sM1R8/fGR/L+Zx7fCE3/PKku9NGNqN8PyoJu9QAP99XEujiPMu0nvZQeKTFxN6XX4/zbrc2UpyP/XuaMvgvd9k7xW8wOvNyn0XSvTYScplwsdY69n74NYmvwdPj/hl53TG76Uj9v7aWXvnCkRzaOrcU7oD/q6vdt6459X5ax32zhgAEmfeffK+ZJHzeZOrBAAgJXrXien8HAIzx3dSkrxmTh16HvSXGkIIIYQQQgghhBBCCCGEWAn0UUMIIYQQQgghhBBCCCGEECuBPmoIIYQQQgghhBBCCCGEEGIl0EcNIYQQQgghhBBCCCGEEEKsBOduFD6b8GZILWnIGTmNdbwmM2HMG/BlqW2e5Dbi9poHEdlrZMKao/3b/8CRWUNkpyGk1+SbfFdizQE/Gtvr1sf1pLDj5E5T5dRpMug1pGJr5TUo8pr1tsRPYmceWYc33om9RkdkTUq3S/qfnxtf/ALVo8r6blDxJkZeI8JqfEb1IreNgYspbxacT/l+F6QHV5vwxr2h07A2Th0/ZQ2Eat4IrS6dJsekkZHXvK2akyZQAIoJXz9GnPJn95qEhSlvnNaSBpwbO7zh8EtXbVM3ADgZ2+fJcyd2Os2aYqepIwuTkROzluH3nUbh333ze0YrnZjkxZ7UaQYbJzZ2hE7DVto0DUBE7Hf3LlPbXWdfnWOBxcL+5mR0TG3r3GmeTpqVeefCbWLpUCxsfJrMpty25Oe5yLnO8nPoNGzOMn4WE9KAunViekniLwAUJfe1PLc6kZbGa1qK0K5P6TRB9xrIeemN9VsMnE7hoaM3DWnM6jhe4DQh9hrAJ127316zbK9ReEu69nrNpL15BK2zgAHJ217Ornicpg2Vwc+o1+Dc+1+QWjJGQxoPAkBJmgsDfqPZgASzuHvua8PH4tpFHk/bwP5e4zR59JpO56RJJwBMF9a+ctbOWyTWoDh2GjZ7TbS9sxsQe69Pu5PqEIesmbfjp87YXoXArh7eXQdsHvAbPFdkURonpzknhq5V4DSJrcld9v/zqxTWONTbnCV4tH9K9cXCzrdyckYn4zFvY43fq5KU1FJOTZk4z8xKU++u4+W6wLnb9vv2eTpkzgCQOk3Smd95z+g3EPcuoNZn3ObE7vsFRydr6O177Tb2PX9n8S7JzwAQeb7Ohv4kOpm3XoFGaoGQn+225nfH0MnBw37PaC8//wy1vX55neoPH1o/ZfUL4IZpZBlvUv3MDTuXF194ntpe3tul+s72BaNtXOC29x4+ofoP3vpTqrMmx947n9I5ct7dg50LL354N6OavKfInfcLixm/o0VOrK3IndPNlcvgxRP2LvRjvNt0B3F+NPDeyTpDbw5tnNne3aC2TcHfpR2M+Hvqp2P7nmnGXoKBv5cEgJTUdJe3+Hug3TWuh86l8vDAPk+R8/ktnBcMWz0eE5Ku9ceh16C+4nEyI8m847x7KtiFE0DX8YcF8amul2/Pgf5SQwghhBBCCCGEEEIIIYQQK4E+agghhBBCCCGEEEIIIYQQYiXQRw0hhBBCCCGEEEIIIYQQQqwE+qghhBBCCCGEEEIIIYQQQoiVQB81hBBCCCGEEEIIIYQQQgixEsTnNVzMeLd5BLZbetPwDuplw7uiNy23jxPbzb1xuraXZcHHrsnYzqecMOJd5ZOEL1MYWj2OEmrbtt5v2i7vaez8njO/IOCDF6EdOyqcjvVJSvU04c8TRWQRnWcsypLqFfOTkD9jlPDBg4A/D9ODivvZMgwu3qB6uxgZrTx5RG2LkxOqz48PqD7eH1vbKff/ouTr1sb2bIUpP1sR8SMAaMHtQfRiMeFjT7l/NdXMaIFziPLxGdXnR/tUr0vrB2nW4/NL+bnIBkNuP9gw2nB7k9p++tOvUP3dh0+Mdv/glNp6X6VzR0dt17Db/vS/bWdJh+qhc145fL+jgM93rd83WpJaPweAs9mc6vnCnqMw5r/3ymuvUX29y5+9qmwOZWEUALKU/0OXjN3f2Ka2nQ736dA5t4vCzq908q0T6tE6efjszJ7RsuJjd3oDqve6di8XOd/H2WxK9bzg8ytLW5t0euvUdhlSx/1rci5YnAKAvOKr3zROLoSNsYGTZ5ktAISttY+cuNESWwBoHPuqsHoYeGN4dQCpmVr+LKi5Xje8TonCrtFSUpsCQOOMkedORCbnKwLf9zbx1s/aFwWvtcOQn7mk6wSiDtFj/ozL0u/yXBu0dm+nC/58ec318cyJS2MbP/Kcr38Y8PmF5CwFTmZunMAZxV4dS86dd38J+T+EpH7zzr9bCTh5uyV3N++u46QSNM6isFohwMepH0ATlXOMEHljO3c6kDrEq02WYXTG795T4tPzGY8z3r15Y93WTACQkTrDuwfEjt91EpLTvHcD3v3diVcba7a26XX5PvVSL9dZkoTbeuQFX+/pzN5fZjnfx9mE1zDzOddHp/YuNTrl9U6nx2vRlNxrNga8XvzCZ/g9ZWNjg+osDnn39OVw7vOt9Zmq8t4POfvt1KZZZPO+V8eWjq8HJGZ6UaP1ls1Zz96Q1CpOXm1anitT8r7r+Zv8PcenPv0pqr/94x9RvSXv4+KY7wHLLQBQ1c6ikMVyQhbYOwoAqCpb28xm/N3F6OSYD+24FHsV05D7+LK0jXNnI3m29epp/4bn2BPN8dFOh8fpjS2bizb2Nqht0/DzfDqxcRcAnoxsfJwveB1bOGvSI3Et6fOYOdzdofrJY/5O6vahnfdoynNLz8lR2z2+JptDcv8ruY8ETi2WkDycOUGr59Syacrfayeh/c3IKyLPgf5SQwghhBBCCCGEEEIIIYQQK4E+agghhBBCCCGEEEIIIYQQYiXQRw0hhBBCCCGEEEIIIYQQQqwE+qghhBBCCCGEEEIIIYQQQoiVQB81hBBCCCGEEEIIIYQQQgixEvB25IQ8X1C9bSujVbXVAGA+n1N9seBd3rudrtF4X3WgrPgYVWk7wlcVn18U8W88aZpxPevYMWJuGwR87DixW1AlKf89Rw+deYdksWrnM1brdL1vG9v1HgCiKLK2Tsf6uuZjeDojDJ31ixOqB8S+RX3u3zsvQeisW2v9rqlKaltOJ1Sfn3J9OrNjT+bO2CVf4yCz69PtOPtR8XVrC/6bmM3sPHLnfBb2WQAg69pz5Pl5SX4PAPIp18PAHozG8SMUfH/zKY9EcWDPaNLfoLYvvfFpqn/mvfeNtnD2t3HOHNleAAA7Rq1ztpbhxvUrVP9ez8bMxskXoRMzs8TGHgDo96zPZJ0BtZ05/jjOrc+cHh3weZAYCABXLvNnH0/P7Dwc301T/uw7e5esdvEqtU1IfgIAtF4ctOc/CPkzenHXG3sxt8+ZF3zfg5CPHUf2zOUFr0sWTq3ROPksJj7VSZ31W4L+Gq8P6sCe43zO1yd3niFw/h+VAPbZvP1LwZ85imxcCxruG1XNY9J8zmP9YmbPYhTzsaPU0TM7v7Dlz9g2nu6sH5GzmMf/pubPWOX27ANAvpjaMRrnfDpHrq7IerfOs0R8kDDgV4GQ+E5UfjL/L1S+4P5elHY9Js7ZOJzweDBy9ILcD0iY+YjQWSMSI1tSYwBA7Nxg4oSvKauzWV0PAE4Jj5jUTVHMn8V9doeWxJyy4vHJu0s04BOndVrrTJDETwAIyAN5OQ3E1z+Svbhg7QOSQ5cldPYq7ZLfd55t4cTeeenc2SJ75kKn3unEfIyS1KatE9sWOT/PVeOc29qOk429uMR/czaz96uq5HW2px+djrh+eGy0w6MjantyyPXD41Oqn41JLTXn9Wx/c4fqw/VNo13Y5rXyxpa1BYA3hkOqJ8wHnXvKMni+XpP7ROncYStwvysrfl7OJtYPfvLeh9T24Kn1AQCoSD6DE9e8cOy9r8lzO7/Ceb82dXJUJ7E1YH/Qo7bXLtr7CAD0utye3fc7He+dGZVROu9RmI95743Y+6GPftP+aO383tx5bxOSd3oA0LBC0stnS9C2Tp5l+dc7ls7ie3vC9Nip4bfX+1TfWF8zWuIUOxt9fk+5fsGOAQD5Gamz+dFH6/zmjcvbRnvjc69S262Le1T/4BGPCQckroxm3O+igOsHUx6z0hPrd14NmTn/wMrTfszP0CBz3s847zRi8rKq/biF6P8P+ksNIYQQQgghhBBCCCGEEEKsBPqoIYQQQgghhBBCCCGEEEKIlUAfNYQQQgghhBBCCCGEEEIIsRLoo4YQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKEJ/XcJHPqN7UthN7VfG28i1aqhf5guplt2e0NOFTDgM+dl3ajvD5fM5/j9gCQJQkVO+Q+WXdPh8j5PNmY6dph9o2nZrqcZpyPbJd6EPwrvJBw9evrfhvBnVjNW9sqgJhQP4l4usU2Ef5yNzZG4T2e10C7pfLUJ4ecn301Gizp1YDgPE+H2Oe27MFAG1M1i3hC1TXfF+D1u5f0/D1KQt+LtqQn9uWTK91xp6N+LN3e/YMJF1+LqoFP8/FhMesTrdr51fx77tNy89W09j1AwDE1j7pD6np5qWLVP/Sl79gtEXO1+/J2RnVi5LbL0rrU80n8Gm7lzoxM7P6bM59N3CWuHFiVTm3/thJuM9ELR+jqe26jcen1PboiPvu5UtXqM72sKp5fK2d1BxlJCd2rD8DAJyc0/CfREjsQxJHASAInT1z/j+JJCVzjPhEwsiJ6bAOUTm+EMZO3nIevkfWsOfEm2VY2+T1QRDaoJnP+VzPTnjcdcooRKF9jjS2fgQAUc7jXVXa+VU8PWF2llN9Op7ysSty0J34kfX4+mUDUkf1eOURRk5F0jj2gR07SD3/5/MOa55DUdhNa5zYFDtnMSY5J4yd+NHNqJ5kfN8T4pfBnM9vWR49OaJ6UdvnXpR8DtMZd8qaFSUA0tjuV+LUUnBqKRaCAientk41nMb8P4hIDQ/HPxwZcWTHjhxfcq5X7gPVpA7yar3aux84x5E9jleqhLFzHtlzfsy9cXMgWRMvvyxDVfLAnpFz7/loFfI9KQquh+SO0Un4OoSh449kOSty7wCAyqunHbks7Tp7cbN2EtVsavPUeDbmP+isa+kUz1HH5qmsz+cRzZ33DguuZ2RhZ6SuB4DJfEL1BXlHM58eU9sf/OgnVH/22iWqb6ytGa119sZ7n3MeFjmvMyryrmo64fek+YKPcTTiNf/9J3aNvvfj29R2Mub3z5AEoNa9g32M4AhgMrLP+fQxf+9Qb25SfXvDak3F92l7gxgD2NrcovpsbOe3scXHGM94vZhX/Fw0ZCsDkvsAICZ5/6P/gKy3swVOSKCxCQBaVks596hloDUDvHuz8xDO+1QvV7N6Ytjjtea13XU+BplL7ZxPXq0CA+dd6PVd649bwwG13b14geqvfumLRtt89nk+Eaf+fvCUvzOI/uSW0SqvznMcsip5spxXdpyI+CIAdJzal5lPHT8/Kvg8nJ+k9fag570D+Hejv9QQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCfRRQwghhBBCCCGEEEIIIYQQK4E+agghhBBCCCGEEEIIIYQQYiU4d5cmr9nWlDTWKgve3KV2moR5jW1mc9soqOs0guk5TWlYT8iy5PMbj0dUr0jzWADo9m2jmeGQN0jyGlrHiZ13kjoNsJwmdKkzv5g0XA2dbi2JNz+v4SRtPsj3kTUs9+bCGqYCgNOLjjcCBG8QFTnNGJdhcfCQ6pN7tnnY0UPesCsfOx1enaaerBl1Be4bjdNprw2s/cJpwBU4TTYDp7lfVNnnqZ0zVzmxIh+TBp7OerSO/zesAS0AtNbea5Ieeg3FQq63pLt1MuCNwpP1Hapfeek5o31hxpvOvfshb1L35MBpbn1q43XjxOVlqJwmfik5lx0nxrQ1n1fg7FWxsH5XxnzdGiefsf5oZcHPxcOH/Oxfuswbhbe1/c3c8X+vIducdIPOSbNhgDcQBoDW2e+QxO/AaSTHGqp/NLbX3MzqXuPjyGn6yjqPFl7jOm/ezrOzBq+t08R9GXKnMWuc2jPg1Qxpyh+6cBqO1oVd5yLn67MY8UapkxN7jhZj/nvlnD9j5TSWa0iXzMB59qTv1EB9+5vZkO91p8ubpAckdgNAGNrfTJxOhQlpsAsAqJ1GxqQJdujUKSmp5wAgIvHTbZDp1GjOrBGQ5pIsx/00ePLwMdULsnaRswFx2qV6P+tQPSBr5zU09O4vtMTybL1u3rSJp99w/GONTeqgtnXOYsD9I2IXKQAJieFsTQEAMf/NquJ6QJKg16LeOzPsnsLGBeBU0EDrbAJtEP+xNux8vPfue1Tvdez907vbnp6OqF6QJqIAsL27a7StbX63LTrO3ZvFIK+ZN6mNACB0GtayBvWN8yxlyfNRTo5oHfG4Ejj+Hzt3o82ebYa7sW3XFAAuXb1G9UXO8yt7H/Hk6T61PToZUX3/kbU/3b9PbX/jN3+D6rvrPNZ+9o3PGI01gwWA3hX+7Ofh6NBpgE3q4Tl5lwQAoxHXb93ha/HubVvz39/nNZPXArqTWR8rnRjYOHcgL34/JDn029/+U2r7wo3rVH/25g2jZRk/43sX+B32i5//PNWnU9u4/gb5PQDYP+JNlb/9ne9Q/YCcgdp5Z8bejQGghZB3F0s73P87Pd6AumWx5RNoFN7r8/o2J/GkLnnc9e6IsZNn18j715eublPbZy7xBvUNmcvpEX+PkTn76jXLzlK7350urwmv3eT394s3nzFass6bnreOf9141o4BAGtDu2cHE/7uwnNIp4Skeui8k/UanJfkLjx33q+1zh54JMS+8orwc6C/1BBCCCGEEEIIIYQQQgghxEqgjxpCCCGEEEIIIYQQQgghhFgJ9FFDCCGEEEIIIYQQQgghhBArgT5qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrAS81TkzTG13ewCIysJoZVVR27bmXdHLkttX5ZnRFjPe4r0sbfd4AFgbDo0Wx7yzeluXVJ9Nx3x+tZ13GPKu8knaoXqc2TGqxnaaB4AGfN6Fs95RnNjfc7reZw3fm8TR09i6ThvzvQkCp5M96XDftnwMR3Z9CmStnCGWojw9pfrseGS06RH3o4asAwCkIT9zbUt8hpxDAMgr7ktJbL9nVjW3rYsZ1cua68xNm4rPr674mZun1ne7vS61dZYP5BEBAHnAPIF7R0T8HACyHo83ETkDXrxJspTq63sXjfbsp/kZjwfcRzrv36d6c/eh0WYl3/dlyJ0x08Q+czfhMakEP9t14+QL8pNltaC2obPfSWznUjsx8OGjx1S/8tCuMQAM+navKid2e894NhoZbWtzk9pGfX4AvHgcRdY+DvkYjRMrKmfeYWDH8c5WnNiz79E4uTJs+Z41JGcDQBzZuQSfwP/zMT/lcTBI7Lw6HR7vooCvT+j46eTYnoFizs/F9GRK9dnxxGjVwokbDT/PgVNutixXR04uKrnvljO7fuXMiR8DJzZlfF2JawAZf8a4x30mDnisTyJbF4ZOnRd560oeM2AigKbg+x7UfOyGTYWn7KWJSl5PNIV9lhh9aps4e5gmXpFgn7ty1t8pbxGG5B+c+FN59apT4EakuPFyFztHAEBCr/u/s7XO2F6VHZKB0tS5A0XO2XVqBRbb3WrfeZ6I/CZbUwBuIVmDP8+CpJLAu6gswTs/+BbVu317t/V8oCicvOPs92J+bLTJ5AK17Q/WqD4c2DPq3Y9L5/5CzxaAlNTOAQ3U/v14PLY5rXTqg8gZOyT1LMDrD3rtABA5Y+wMBlTvd+1crlzcoranY/sOBQBurdm88/b8kNqeHOxT/Zt/+IdUb8u50bY3ra8CwLUr16h+Hh48ekT1pmQJii/+yan1AQA4GdtnAICysvu61ud1mvcuIyBndL7IqW1ecH9saidOE/892D+gtn1yxwaAixftOff8/8r1q1T/lV/5a1QvySVtuMbjx4HzbiXq8Nz/1pvfNdrhIX/2suBFTEP2LO7w89nd5Geut73Lxyb1r3NdWootEncBYJbYJLlYcD+qnfdG/S5fi1ee2TPal16+Qm0vDPl5CUiOmi94Xiic+VU512cLu9/zgt91eo+eUn33sb3X92isAbIN7hs7F7hvvHrTrt/+KX9fOC/4M3rvQtl9P3Hu3t6dvGysnzTe+xm3BnLiIblk1AGvE86D/lJDCCGEEEIIIYQQQgghhBArgT5qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrAT6qCGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLES8FbnhN1Ll6i+ntsu77PZjNqWRU71puXd3Iu57U4/G5/wMWo+xqDfNVq/u0Fts5CPgacVlVvSVb4uF3yMlneKb0lHeKeJPZqWf4OqnP8gikqjpVnKbZ2u91HIu9A3gf3NyntGZ35k+dA4Y1BjAAl/HD6P1q710jjPRl265s8QRXyNA2+6ZC2aihuXOffpNrA+nbR8fvOc+39e8N8MQrsmbcPXqS75/JqU6M56JCk/F03I9Taw6x04e5CFfE0C53NwlJJzRM7KRxMsqBxG9jfXNm2cBYAbL/B594ebVN/a2TbabM7nsQzz+YT/AzkYYej4kbPhgRMjAuJjYcP9qxPxDUxJHFxUfIzRiOeiD99/n+qXr1w0WrebUdsG/BlnU7uuRc5zzmAwpHqa8t/MSG4IHf/PFzyXt5WzN2QcJ6Qj9AIf+Q/SJKGmUa9H9brisSwgB9qLy8swG/Oz1pDfbwZ8n5LQyeEBz+HF3NZj0xO+f8WMz4/F6cbL642zsY5P0912XKCBrWkAAJX9zbzmdWjr5Mqma2tFAEi61se8GFS5dZRz5pK+nV/E403o5OemsD5dBXyd2ojPO/RqEHLmovqT+X+hdjb5mU0mc6PVDT/HmfN8mXPTqUlcCp2aF875YjV8W/P5xU6uC0nN9JG9XevAqWNJWfORzgKtE9e9Ejlo+D9EZJzICexejRU5E2f3Qq+Gb8HPDJtfGvP5sboLACpwPU7IOfgE7hiH+4+pvrZu41u3x3O+l8cD5+49OzsyWlXweDrr8t+c9Dr290J+hvLSq0H5uUgzG0/Tro2lAFA7e8JqqUXBa6kw5vE7znjOYHg1U+08e5zx2ubChl3vfpefoc0hX5MXnrlqtPLsmNoePLhH9bbhOeb0+MBoccjj4TLcfvCU6vnM7mHs1PuLgvv/2LkTZR2734Mhr8f6fZ7PQOqjRe68G3PeGbTg+33jxk2jfeZTn6K2vQ73r25/YLQw4f4/6PPzfP3mdarnC7uu3vurifPe4fozz1K9jewc79+/Q22fPH5C9fnc1hphh+9v0F2j+qLl61o35I7xCeSLOrfvTQGgIvGnzp37iDOtzMmRz12ya3H5As8LmVM7zwryTtG5v7fOBMOUr33K3tU6tVjr5Jz8dGRtnbtq5MTuzc0Nqv/Nv/oX7fycOurNdx9S/WA0pnpA6hfvva6Xn8HqU++9lrMmnquHsZ3LcM3GoPOiv9QQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCfRRQwghhBBCCCGEEEIIIYQQK4E+agghhBBCCCGEEEIIIYQQYiU4d6PwtTXeFKeqbfeP/tBpUug1+k1405LF1DY+OXh0h9qGLf/NrY11o+1s8mdZ7/B5dGL+7efk9MxoOVkPACidBmRlZecdRLxpVFXwZ0wz3pAqTs/fRbuT2aZuAFCThtIAkNPmTk5TH6eJO2seHTjNe2KnUZXXPD0kfXqcnkNLUZd8T6qx3e+GNMn6aBD+DHHG/THukIZTTm/Wypkfa37sNYt3tg9wmg31utbvYq/hMGnqBgAN2awyt028ACConKZHXqNfckYjpzNtWzoN7pxglpAma5XTuCuYc59mTeC8Ruu99Q2qX9++RPWLz79otNmU78EyOH1cEZJnS9lhBYCYP3PhNBpjTUG9r/ZeC+iE+EzjNOgtneZ+jx/xxp4JaWp948YNahvHPHaXpNF1SZo4A0Ds+H/mNVMj86NNZgHUTjPjwlkrdl48n2bNBD+ai9Uar2k8abwN+DmRhSevmdoytC0fsyXNI+vCa87L9ap0zgtrzuissdeINyANi93G386+NjX/zTCyeSdy/N9r4tySmN7M+fksHR+NnAbsQWTPBW0SDKApeQ6NnRqXpdy04zT1dar1xfz8DW+bmAfmKHV0Ul+Fzfnryo/DpW3e1BaVfZbFzD4zAPRaXscOE6eZcWV9ddHyhW5iHjez1O5XUPMxgo95ZiJS2FWVV0c6DeYjcqa9xtFut3Hu7xFxysBpFBk6ucQNs2QfGueOUZF6FgAi0uAy8Wo35xm9FsesIXpA4viyPHbuvKcnttbc2r5AbXe2d6ieZM5ZJgXc4pQ3ks4n9h4MAKcfYyly557ilRMpua/GCX+WymkqO1ucv+7tDe17BACIOzxm5eSuns94o/X5nOteY/H7pAntlUu71Pbm1T2qx6TJ96bT3BZr/BmHPb7enZjUegvexHYZnh6NqD6fs33lZ7t2mlRPZ3ztY9LUttt13p0474I6ZJ27Hf4OZ2eb7+vW9hbVP/u5Lxrt2rUr1HYx4/fphDRVX5AG2gAQBtw3woD7UhyTOrflETZN+brubPEYt7Zu9WeetY3TAeCdWx9Q/eHjfaMVznuR0okrY+fcliTGxbSuXo7TMX/fUJB3GY3zDMwHAGDDeSf1zKUNo21v8ZqrdeJuWxM/qPj80i6fx7DfpXp3YOeSpvwZB8677u7Q6rMFz1vzCX/G4S4/ty986g2j/SqJNQAQpd+m+h+++S7VJ+QeVDo1UO2872I5tHXqJTj308CpcROSt3d2edw7D/pLDSGEEEIIIYQQQgghhBBCrAT6qCGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLES6KOGEEIIIYQQQgghhBBCCCFWAn3UEEIIIYQQQgghhBBCCCHEShCf2zDindgB29E8CG03848sA6qnMZ9GGln7arFJbdt8QvVuaufdcZ466PN5bwy4Ppva+c3mU2o7L0qqs67yYdKhtnVdc73iY0dFZrSm4WMEzt4kkffdy867aSpqWZV8fm3DfIf/WhjzPcg6fL2jxNq39ueWpi0K/g8LqzeLnJqWU74nbI0BIF63ejHnY89mcz498pNByBc/cc7nYNijeq9r7ZOY+1dT8mdcTO3zePGjdmJTGPF5t+QM5I7vLgI+dlXw9e6sDYwW94bUtgn4s9et3QcnfKB2njHp8ziZ9uz8orTLB18CL540tX3mqnYOJl8exI6fxgH7TWdsagukJN61zrNUTkCZODngbDw2WpzYGA0Ag+E6n19qc4MX1vxYzxc2IGsSOAE58vwuSbzZkLEd05bPryrtGfWePXbylhfjgpA8O9GWJXFye0UepPBi42JB9dkp16uc+IHzbGHLdeYb3uI3leN37lG0e+KkC3hBoSxtvq1JrPm3v0jVOuFBtiQ5IADPC4nz/wmFmROzSK5MIx4T0g4foyE1SL1warGI7w2LywDPuXHs3QWWY9OpJxanp0Y7PLWxFADSOY9LWwHPh4O+zYdnLY9hs5qvPwtXWcLnEYaOTzq5JE1JLVA48THnekLuUXHqzM+J64VTogakVomdFJA5Ad+7YrDyqCH3JQDIvTFIvEidQJQkTs3i7M2cPHvtXWCWYP/JE6qHZD2PD/ep7cn2NtV3L+xRfW1gz0VR8bsOucZ9ND+SY0KnVq+cnO8VCW1p7zU8+wGznM+7JHVn1uExKHJi3nzO71enpNYrnPtflXPdG5vVNlHLc1c3cuJKaA/0+OyE2i5mPNZWC37QF6dHRmtnP/1z0TjvQ9h9oqicfOjUKq1TJ6aZzc2e7aLwftPObzDkOf/iJX4+L12+QvWtC/acd/vO/dNZk5PDA6MdHVkNAIbrO1QfrK1RvTOwc2lJfgKA2qnTAifBbAztPXZrx8YxACidOq1K7BiFU4+dzXjEmTx+QPU6t2fUu18tgxdL2T3WuydtD/k95Suff47qn/3cZ4zWiZ378eEh1WtSw5fs7gIgcs5c1OG+Mdiw/jjY5L6RkXckADCf2Dj97tvvU9vh9ojqL3T42CE5A9sXLlDbr/zcF6l+5qzVe7cfGS13zr6XhsHu3iSHAIATltE4ubzbtzl3x3n286C/1BBCCCGEEEIIIYQQQgghxEqgjxpCCCGEEEIIIYQQQgghhFgJ9FFDCCGEEEIIIYQQQgghhBArgT5qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrATxeQ2TOKV6GNt26UEY8UGcjvVt23AdtuN6t9PhY0e8mztaq8/nU2pazCZcd+xnkzOjTSaObcHn18B2hE+cR4xT5x9Kvq4BaUNfO+3tiyLnY4Pbo7Fj8972QEPmAQBtY/e9bvg6tQH3qTDkLhzHVo+ic7v7uWFrDABBTFbDWfvc8Y1yMqd6Ss7Roij42KWz9gHxu4D7Ueoc506H/0OW2rHr0nnGkvtdWZd2fs43WCesAI3j6ws7dpnzeTjej6rk6909OTVamPX4IIsFH7u1D3Q65r5wcjamepL1qb596brRssGAz28JPH+cLOxzTOd8HSJn9dPQiTSR1R0XcGMVGzqOuYM1NR+8Ir4LALPFzI5BchwARN5vkvg4JXkIAEYjni8aZ35pnhmtdRawrHhcKSs+Nl3XJKG2gbM7BTkvRcXXL3aCQpTy38wy++xxxOuSZYgC/vsV8YMy58+2mH28tU969jezrn1eAEDN121S27qmmfP1CQLuMy0ce5IX29pZe1J3AEBbkmd3hmgd362dmNWQOsWbX1Q5tayTK4PK7k3s+EjqFIZpZGN9QjQACAPuU0HL1yQM7fOEpLb6adDN+POtDYZGmx0652hm8y8AzE+fUD3btuNs9jepbVTxuDQmS9o6CSZhdSGAqOZ6h+SBfsLPbuVsC9vb0CnqvBq5dmJyRB40cOLmZpf/ZidxzgY5d0Xh1Nvg/tDr2Xtr5uRWJ50jL/mzH07t/KaO7TIUM177gdTw+YLXseMxrxNPR7x22NvdMVri5OvUiQcxybVxwm1b7/+vdOSqtJtVOXexiVNfViSGz+e2RgOA0eiEjz3lY9NxnPtVHHP/b7wcE9lxzk75uj7NnMtbYec3OuAxslzw9yIX1ngcKufkvvMJnIvQWTfmYzV5DwQAacrfaw1IzvnI3vp04dyxK+e+35B4nDh16XCNzyMkPgAA04ndqw6pbQFgTu5iAHBKfP3o6JDPI35I9Z2LV6m+d/WS0eKYP/uZc6+Zzrk/griDlysHgy7VN7e2jDaPuI8sAj4PJ5WjIvmMvadaltiJM1lmf2t3g7+b+KWfeZHqv/LLX6b63iW7r7PxEbWtnbVfkDw3S3k+8+6IgZNfKvIuIXfe+UTOeRmN7PN8/+1b1PbCHq9Db772KtUDFssqfh/Z2d2m+s998bNUj8i94dDJ+5Vzfzkd2/vfzHl/VXzMd2k7u9Z3rjzzkmP970Z/qSGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLES6KOGEEIIIYQQQgghhBBCCCFWAn3UEEIIIYQQQgghhBBCCCHESqCPGkIIIYQQQgghhBBCCCGEWAl4q3hCp8s7wrekpXkYkU7uABDyn2vaiuolaqOlCZ9HUc2oTjvcV3Nqu5jwjvCnp7yT/YTYT6d87Jw/ItrArlUQ8WcMGt6Zvq344E1g7duG96AvecN6tM5vorV6FPJvZN4YdW33t675s9RtQPUg4HpI9DhJqO0ytOQZAKCt7DNXJV/7quDrU7YF1ReNXecZ+T0AiEO+Pr1earQ+0QAgifl5Trte+LBzyefOmZtyxyvy0mhRdu5wBQCoHL+bze2eeeeiIecTAOLUzg8AUD820unJlJoma0Oqt52+HWOyoLa3H9jfA4BFwf3y4tUDo129eZPaLsNkxuPxdG6fY5ZzP0+ceBKm3A8ylndCvn+1c15K4jMN+BnyQmPt/ENV2uesCif3zb1zYdev8Xy34j4wHvH8UpbWp6cz7rtzZ95uuiBTDJz9DeDESbJ+3rOnTszqDwdU39jcMtr6+hq1XYY44zG2mtuFK0q+f/CeOeP5Le5bPY2cWmzBfb2a2bUvnZjUtHzstubzBsntXl5t3BqIOhi1DZz1ax2fZvOrWn4+F05caQs+l3Ju9emEx87Zgq93ktr5ZRnPLXHPyeUZ3/coss+TJk59vyRZl5+Nre1tOwdSfwLA6Gif6vt336d6ee++0a4892lqe+HiDap3UxtP57njYyX3Mc8nuySODZxzHvCwjpbco1onL3r1NIvfAJDF1j5x/GO9x89A37EvC5aP+Dx6ju9sbth4Hzq5oXTi02ThPHxs55flTgJcgsi5T7NY2Nb893MndoxGI6rXlX22xLk/JZETZ0mOcdzLrZm8/6BpiU87dVrlrAkd18kZpZMbpk6dy34zzTrUNk2578J5HmafOPe8yMmj5XxitNnZCbXtd7j/Zc7zJGR+EYkTy+ONac9r5Phov9+l+vo6rxMLUlNPR3YtAT+Ps7oudGrhbpevcRjymHQ6OjZa7J1P78yR+3ux4M9ydGTzJwCcnhxRvcjtu7TBkNfZx2fOui743Xs4sHsWOfHDe6cxHBB/cHJlMud1bhjzPYsyu+9R2qO2S+Ek6x65N3/hpWeo7d/863+J6ltb/Nmmx4+Mljt3x6bhMSkh8+s55zOEE5N63J794sLJiYj5vp6OrO8en/Fz0ely3y1zbt/mdoanx0+pbR3wZ+yRewAAbA6tj1VOXqgb531qbHN/NOH7O/HeyTox7srV60Z75rkXqe150F9qCCGEEEIIIYQQQgghhBBiJdBHDSGEEEIIIYQQQgghhBBCrAT6qCGEEEIIIYQQQgghhBBCiJVAHzWEEEIIIYQQQgghhBBCCLESnLvzbuI0p2MNdpOEDxuEfIy6cRrukIZFaY831mlq3oClrG2D4pI1DwcwOeONT87GXJ+RZq6l0wjQ6R+JNrDrx5q0AX7zTJCG4AAQkgZHTj8xt7lQ6zRkZE0/vYbvbiNb0hTca+rmjfFxcPoTLUVxYhtzAUBOfKZ2fKN11r50GsWWgW3aGjvnc+g0cxuuWX24xs+W12TNa6DVMD919tVrOZqS3/QaR3vNEb1DF5L5Bc73XadvoNvMeD618SZ3Gtptrq9TfefqVatFvJlgjzSBAoCn+7xRW5rZB4qd+LEMeXn+Rty14/+h1wjQaXKcZbbBepbyc1E2PF80ZC6t4xuOOyJwnIY1Zw5rHutnY95obFFY/5qcjqhtOed5y2tMeXpqG6FN5vb3ACAInLNPmioDQEPWsKycJukFz8+sC7kXm0KvcaZjv7Vpz+K1q1f4PJYg7fJuvqzpa1PyZ4gTvn9eo/CINOKNvMBWOM1WWe5y4m5dOLnaaUJXk6bK+cJpZDfj56UmvhRGfD1C2jYQiBw9bOxaBbUTm5ygUDrPU8ys3rT8GccnI6qnXfucw20bCwGgEzt5pMftU9IkNnQaFy+NE1NS0vxx4xI/m711ng8/fPcdqv/gu39ktMvv/oDafvYX/1OqX3rlZaJy/8idBuJNbWs6AEhI08rtNb6HfdJoEwDSiOQ0L3k5dXZLanUA6KZ2z2Kn8XfiNAtunYa1J7lt5Dk743VN0HL/3d60zWO9psqBs2dJxM8uC7dOr/FPBtKQs3Hua16JVxTc705Gdr+9e4rn6yzXhk7+rUoe87znCcmzx06tVzubwtTAORfevi4WfP3YWtXOfcR7Z+Ctd0jiZFnw9ZtPeR3ZlLbGCpxadHtrj+rXrtt7CgBc2BsarVg4Nd0SFCVfe/YOwatXg5bro5Mzqj96bJv3Pn58QG3HTqPwhtRBe84Zynp2LQG/6W5Dnr1y6uluh78byFKr1wVvqjw65M2MT44PnfnZcS5dvkRtz8hdGgAap7l2r0saTXd4Thw6+tbQ1hqJ85ZiNOX+l/Z54/OiJHXkJ9Ao3HsdUpO72d7eNrUdrnN9Meb7mp+NjRY4zdgj571RlzQFD527d9tyPXHutiV5+ZfnPN4FIT8v87ndb++e2SHvWQAgc/QFu2c3Tl4APxe9lG/85R1bA2UJn0ce8PXbJENPnPj25AmPh7XjmJubW3Z+Gb8nnwf9pYYQQgghhBBCCCGEEEIIIVYCfdQQQgghhBBCCCGEEEIIIcRKoI8aQgghhBBCCCGEEEIIIYRYCfRRQwghhBBCCCGEEEIIIYQQK4E+agghhBBCCCGEEEIIIYQQYiWIz2uYRLxjfQPb0TwM+bcSr1N8EHD7gHSy7/V6fB4575Zekg73RcW7x88LawsAi7KmetW0dh5WAgC0Lf+HlnS4L/Kcz2M2pXra4WPHpJF95OwNwPcGDe9Y35B5e9/IAle3v+muk7uunm7/oW3ZnJdjfnBM9eJsZn+/4v6Fmq8xImeviBzH3Dbt8COeZonREqJ9NA3uG1XF59029rwkCZ9fv8vnV5f2NwPHd4OS76u3fIOePRhJzJ897fO4End5HEI2sPMYrlPTCy+9SPWdlz5nf683pLZ7L7xM9cnxIdVLEuOC4Nxp4Nysr21Rvdu169M68aGTkAAGIE35fKvW+kxd8dhdOoE6CG2eC538FHl5K+BjZ6kduyp5rD875frx6YnR0oTnZufIod/nvlssbMxqKn62nEdHGzh1QmtjReXsTf0x4kpNtI/GdnL5fEL148MnRisLnm+XwXENRLFdt+7QOZetV19xvantHgbOuv2/2fuPX9u2LM0P+5bf5vhz7TPxwmaYjIysSmYWK7OyDFlVIAgBMlBD6qgliC1CgFoCm2pJDTX0D1AGECgIEAQJKJCCCIoGYBWrlJUuMtKEefHs9cdvs/xS46YAQuP7ss57+6WgLXy/5jjzzjXNmGOMudbd+FJxto7Oo8+kqi5qxP6R8wkAzV3cq/5N9EUAGAe+r33XBltGfA4A0lHE9InPJxnipqWk7gWAPOFznET7to3j7kWdknZ8fwcy90msU1fz2ne95r6+OIxrVVY8Lu+MiMlsVw4Ol7Ttowfc/uDx+9T++uJVsL0kNgAoDr9N7e9/L9p/8P4RbVt3fO3WN3xflgexLjk74H2cLEQeyKNPqliRiDOj7LTGEvXiJO4S25r7e72NPvny2TPadnEQ6woAODiI/nAofGcSSU2Nm5XcpZj7LqgcN5HzItKLtg8iB5OYMoh7SkpqJgAoyf29UvdPETfVvTQl7yPUXbDreCxk65qLe0CS8rzI7rAAr3dGsda9GN8o4uE01sHWtdEGAKs7MW6SY2ZkvwCgn/j+Lo95jX/+6FGw1es72nYX2pbXyOw+U4ic1ZLcDgDPnsV6EAB+9vMPg+366pa2VeelKGdxHL24Myx5XCtyvq8NeUfUifiV1Hz91qtYI9/eXNO2tzc31F4Ln2Z3o1nB557N+D3laHnC7Yu4Jgvx/qNfxj0AgI7UqMnAY9ai4utXiHOUkn1PZ3x/d2EQgTAjyfrddx/TtpV4v7Hd8LXIy3nsY8nXYRz4ug19jL2JeDW23vA+VsR3AWBL3hGpeHwo1m/bxBgrwge9qwJAu+bnpa9J/d3xO1A54/VLecz3LM3iHj/Y8ppr0/NYvyV38q24/z04O6f22xXPUYtZ9J3tis/9PviXGsYYY4wxxhhjjDHGGGOM2Qv8UcMYY4wxxhhjjDHGGGOMMXuBP2oYY4wxxhhjjDHGGGOMMWYv8EcNY4wxxhhjjDHGGGOMMcbsBf6oYYwxxhhjjDHGGGOMMcaYvSC/b8NUqLz3TEE+SWjbiQurI035t5UU8aFFxtsWBZ/K1MWHdmJ8CfgAZ/Oozg4Ahz1RkM8a2nbbdNTedrGPYeDK9Nt6S+2j+DY1S+J8spSvU5Zxe5LwvjOyZ0nK1y8R+ztNcX/TdBRtqRnjxNsPY/RLta67sLlcU3vfkmcNfBLDyO1iOQGyJ2nKfXogZwgAJrIno1jkoSNnHMDAlx5JFsdSHfAzBOEbm7tNfF7Lx6H8rsi5PcsKMgw+jkSdl6Kk9nSxCLb5+QPatjo4Fc9kZ4vv7/yY9zE/PKH2aYyb1nctbbsLH3z7+9R+s6mDrd7wM7SYVdT+8tkn1P76zctgG1iMBuTn/JL4zKyM/gIA4tiC7xSQJdF/m3pF2zY9P1zdNp6LRXlE254fLan98JDbhzbuTdfyvNUJn+kmPvueHN2WGQEMou+JxfSR768aX73l651McZ6XlzPadhdECgDy6JAqfmFUa8zXoq7jvoIvPdKM7zfdVrE8JPy/7VvUVyy5p9e8aQKRdMiEklHE9ImvUzbxRclIvp1lqtbho2vFeU76uN65iPUqR7HNabfc/4eB25uax+DVVZznfCly+Y60oqAYSX7PZzwmnxzH/AsAv/W7v0vt/+V/9p8F24e//GPa9i/+/J9T+298/HeC7Xsf/JC2nZ/yQ7M9EvcXsuWHM+57Zc79JieXNxVaUnE3GkXgYvWEKMllLm623PdaErdGEeOev3jNH1rE9X78mNdj8znfmzHltR7bHFWH78LA7tjg94ZU3OPygp+XXNSxbRfj0jjx2KHudylxsrzkNV0+8fFlIpmURexb1RN1Q/If+H1Q7V+Ri3sAydsAzz1qnVTFmCTizJEz2ouz1arantRNXcvb/vzDj6n9P/0v/itqv/617wTbTLy3+Yd8dPdC3W0L4jOJ8PPVHZ/zi9cX1H59fRdsmw1/LzOIfJY28R3Ri5fPadtNw/s+rfi9j93hr6+vaNvt3S21Xzz/LNjevOHxlcUJQL+vSdm7CxGzKhErFjO+lyV5BzgTd8ileKfRsC3ruO/OcxWz+DPTNPpaWn31tdRS5LFvfe1JsP2Nv/k3advF8Qm1tyvuB9MYfTolMRoAspLPedzGOK3eeV5e3vDxibsRe0eUipg0iBQ+kDqqKngfWxFLX33OY+nYx/Vbb/m75BmpuQCgVO+wyN2oF+e27vhZ7MjL+9mcv0c4f8JrcLzhcYi9A9yseU14H/xLDWOMMcYYY4wxxhhjjDHG7AX+qGGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2gnsLhY8jFxbp+yjuIrQIkQrR3bLigjtMKHwUIilKhI6Jiyq0YBEX3jlcxvElKZ/LNHHRF2bvpSg7/0PGRKkBdGncs0yMTwmCC60yJESEXK0fawsAI+tciHgy4W8AGAbll7GfRIjY7sJmw5+/qeN4Nw2fw0aIiG5bbs+JQGElxLJnQsyKL/0XEwpXfpoSwUqlkdeJOW7Xcf/qhq91JQSflFBsTwQPOyFC3olJlgsuYHX8hIiQn/I+OrGu9SqKYFXiIGZCALIQIk4pE0kXYnS7cHB8QO1f+9a3g60UorszIc54fcVF/NZEaKwTglhMVA4AKjKWmRA8U2J46pktET1b317zTkTMLIn5gVjrp4/OqH1W8blviN/dCRH3puOT75XAOXH1phM5WwijJkTIeVT5gggwv7Xz4iQlfatx7MIoxKh5XcPzVSaC6SSEKQcirj0JlftU5MiRtc9FH2LclSg3M/J/awohwJcJMeRijHGtUKK5IldmYk1KctBz4eeFqKMmUbux8zKIXJmJPNczgdxUiG6L/8fExLgBoCd+2Qkx6V1phbhiTwTmb3NeTx8ueK1++uSc2n/zH/zbwXaxekPbXl9/Su3/5P/8vw228/N/h7b97d/6PrWfHfE8ztwmI+cZAJqWr0lH7lGzioutKp1rJtoNAPVmE58n7mLbhu/vzSr2AQCrLRGrzvn+NiKufvIyilOyfgFgccjzaLk8ovaE3AsncY52QZQZ6InQdSFyw2zGxTsXh3xu222sVW5vrmnbQeTxjtxLy0rEbxGry1LYSf3WDdyPtMg9sdGWQCrWVdWRLG7SHAogkU8VdmJW7z/U/ZgJhUPUO69fvaD2//S/uKb2P/3TPw+2Rw+5sPX/6H/Ch3cfJvEuaGQ1jLgLtjVfn6YWuYis8yjqLiXezu6l9ToKkAPA9o6L6x5XXIy6beIZ2NxyUeWr13xfb68ug60UQuuPHz2i9sWS3z/f/+AbwfbuB+/Rtq1631XysVRkjGUhhMLJuzsAaMgZVYL0s5zHellfktowUedzB/7b/+hfp/b/zn/zHwfbt374N2jboeY+o16hTeT9RCqKiTQRYtRNFK5/9YLXYm8uV9R+dHJC7cdnMc9VIrcUJa/FDohA/dkJFz0XXWN9y89zRmpqdf8cOv6+Zn3L23/2eTzPf/ZxtAHAdc33bMhirfPwQRSeB4DD0xPeh3gvcnMd16Su+f7eB/9SwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF/qhhjDHGGGOMMcYYY4wxxpi9wB81jDHGGGOMMcYYY4wxxhizFwiN9kiaiQ6Iwv3Q97TtME3UPg7828o4jcHWNlz5va4bau+JvW1a2rZruTp733NVeTI8jBNXj5/A556kce55whe7yAvet1jXro3zzDLRd8H7Tsn+AsAwRvs48nFM4OvXD3EBien/3YkwC8dk/yDhc9mFuua+vl5HX1q1vO3dwCd3J+acD3EeZ1VJ2y7EnNPk/t8zB7EpXSPmvqmD7eKGn9vLm9gWADZ1XL+q4GN+dDSn9mni7es2zme95jHhruW+W264/UF2G40nd7Tt8imPWSWZez7j40sTcWCGGTUnLJCPfBy7UG9X1J4Rv1vM+P6VIumUsyW1T8TXWQ75q0hIvMvEOCbRdy+CVUbGl8rx8T5IukCZibYDP589dyXkWcwBS7E3/ShyqJjPNMbzonJRVvJclCD2PYrJpKlYE/BzkZP2i8MFbbsLo8jVA6kxVIhORX1QlTwHYElygMg5CUS+7+L4upKXjyNpCwAV+L5WbZxo9olK+Ny/8pzUUeywAEgnUY+I2nJAzFFjJ9Z6Js5cx2vLtlnHtuSsAMDUiPUmcxcuglnBfTrLxLnIY0d5yvdxV65ELdV10Rfual43tD3f8yrj9pNvfDvYHn/rb9G2zU//GbV/8vM/Drb/w//u36dtT4//XWr/wXe+Ru0s4G+FL11dXlP7+uYm2DKxHiI8oW/52cgQ92wQgWsj9rcV19Bxir43lge0bXnI/bcj/29vTepnAFiveC6ZgddHKelnTO99pb43hciHHblnq7vgJGJeLvwgz9k8+LqNI9/XaYp9lAVfn1LMUcXwqort256fi1TUGTmZTllxPyqritrZHRYARnLH0JWoeC8i1pW9XxlFrafsaRKfyWI9AOQZ37NE3C1r8t5htebxYxfG8f61cydiZt3wPNI04n0SWftB5OtUrA/z9XeePKJtl0tef49k/1TftThDmcjjh0fHcRwLPo75jNcTBwf8jnZy/iDYzs4f0ra1qCM74dMFiVlzcZ5TcadbszpXvBdUeWQSOSAhMeuvo5T6H/+7/wNqf/LNHwWbiv/ba35eT05Pqb3J41kk10kAQKveg13HdwbPnl3x54n7y5N3uJ8eHB4Gm3pPfX3J39dsttEPTo5ivwBweCDusJm4N5B7TcESFIBEvGO+vYx3CQD4g598Emw/+fiCtl2TvAUAHVnvqvqQtj0Ua8LOJ8Dz8xd5Pxn+7Zf+l8YYY4wxxhhjjDHGGGOMMf9fxB81jDHGGGOMMcYYY4wxxhizF/ijhjHGGGOMMcYYY4wxxhhj9gJ/1DDGGGOMMcYYY4wxxhhjzF7gjxrGGGOMMcYYY4wxxhhjjNkLuBw5oawqak8RldizfKBtRy5Yj6LgSvHjFPvJiFI6AExEnR0Atk0bbA1RsQeAuu2Fnc+nrmM/m218HgBsW26fyKLM5nPadjlfUnvT8fn0Q7QPA99yNg4AQBr3VzGN3N4PfP36If6DQYxjEt/f0oTbpy8w7l3oGu4zLbFvhH/ddHzhVgmfQ5HGM7Ds+Bon4Os5krXvyFkBgJurO2r//AW3f/RmFWyf3jS07UrMfZ7Hub97zM/FYj6j9nbka/LyNo7lzW1N227JOgFAUfK9fNm+Cbabice3g8ePqf3wUbSPI3/eNHEfmcjZB4C+3wZbfRPHvCttw/d7bOPz1xMfK+8BmM14DFsuDqJRBKU05XGjJ3lkW6uR8LOlIs+8KoNtueA+3YmYvt5sgu3izQVt++rlS2ovRR5Z1/H8Jxlf6zTnc09FrC/IquQFX6lS1ANA3Mu25X2QEAkAqEq+74tF3JvjkxMxjh0QaTYhsV7lsEnkhbzge7Uooz1V/59l4gMcSH7penFu1/GMA/pc5FUcS7ngc0lIXgB4/cfqRwAYJp7nhOvSvoeUx4RBxOltx5+5Wsdc2Q28j6yIPgoAKanN1TqlBd93Vd8XZXxmlqnzuRvtwMd8tYnr0Yha6pML7nvLigeE169v4zgg7hglj5ub26tg+8mf/gFt+7/53/8H1P7f+rf+MbU/fe/dYMtEXPj444+p/cd/+EfB9vLFc9q2afmZfvr4IbX/xm/+zWA7e/iI951yv6nFvo9pjAGTyP2JcMl0jH23Igj3IvZ13NWQsqVKRBDZgUdiPe9WsRaYxH3o4IjURgCOTw6pvWpiTb3dxue9hc+5msWYsljwWj0TuQsjr99KktOWEHXNhtf2A6nt1d1b3aNqcWfqSW5kzwOASfjdKO4vQx8dUvWR59wfZqQWPTs9pW1PDrnvHB4fUfuD0+hTc+ILu5KJGn5G5rze8rzw5uUrar+9uqH2ltTIQ8/3Sb2renh+Fmz/+m//Dm17esbPfi/uzWUV/WB5dELbTiQ2AkDfxvMyV/VBzuc4JNwfV6t1sJXi/g6RLxLh02A1tCy4+bg7cp++Ee/03lzH2g0AWnH3zpaLYMtFPNyFB+98g9rLMq7n0Mb6BwBS8Ji5PBR+kMVY0Nb8DDWra2q/vLgMtts1X3txBcJ2y+vy5Co+8/VrPo7nr/m4syz63ekRj4GPnz6g9lnFB17fRV9qRY0Lce9Sa/WMvI+7vOHxsBPvXzuSu1j9AQBX19fUnql3CaSeVW3vg3+pYYwxxhhjjDHGGGOMMcaYvcAfNYwxxhhjjDHGGGOMMcYYsxf4o4YxxhhjjDHGGGOMMcYYY/YCf9QwxhhjjDHGGGOMMcYYY8xe4I8axhhjjDHGGGOMMcYYY4zZC+4tMZ6mvGlB1NzLSnSS8G8oeV5Q+zhGxfV0EursLVeEb/uoCN80vO2U8HFMYu7DFOfekTEDwDDwcRPhdxRiPUqxsINYk3EciJU8EHytASCdMmqfpth+Snjfk3gmyLBFSySi71H4VDrGztUcd+H6pqH2y1X0u5uO7Qdwy7cPWcX9bpazteCdNE0cBwBMxGc2Gz6XXz67pfafvl5T+/N1fOam5+NL2AEAUBVlsM1m0QYAScHX6abh6/3x1SbYXtzxuTfibGUpX9f5OsaWl2JdHzw9p/bH3/xasFUnc9o2Jev09g98Tfr1XbBdffpL2vbJb/Cu70O35b7RNtHe1Spm8v2bz3h8PD8/C7Y04fvXd3xPui7u393Ex1HlPDYezPmeLBcxfh8uZ7Tttubn4urmOtheXVzRtvPPX1D78dlDap+yuK5TJvJ+weNuBb5WOYnHqh4oSr6/LMYVpcjNPd+DTPw3jgXZm2q+FOP48nCPAZIi/iVNeeuR5F4AGNBTe5rGSSc0hwB5wdc+H2Lf41bUDD0fd0v6ePsPoql8wGud+S23d3fx3A4NH9+o/itPKtaV5ICRrCkAIOH+P4qNn0j+E2EPiXDech5jyDTw/NSsY+4DgERUXhOJn+VMFfi7sc0PqT1bxnm3E4/fN9c877y+5utxdx37SQ94Xj55+ivUnhaLYNusLmnbT37Jc+1//J//M2r/3q/+erA9fHhC297e8rmvSJy97PhZ3Kx5H1l7Su03U4wXs5T7x0r4WCdyRkICQytybivqSHY/YLUvALQsRwGoR3E2SH2pztEu/OiH36f2IYlr0YuxlgteZ+SiPrq5i37Q9/zefHstcgbZq7modzJRC/Tk/g6Axuo0FRcpqHsfsYv1UPfPIufjzrJ4BlgeBoBM2OWdnL0zEDXTydEBtR8dxtrmYM7vGIuK28/PT6j99PQo2DJRV+zCNPA9adsY29685vH45QteI69WK2rvVXImFOJe+v1f/W60/fDXaNs8F/VOzfNfWsX2R4dxPwDg+JjnuXYba4S+rWnbzYbniw25ZwLAto7nWcWm2QEf96zk7Vn4bsl9DgBWDc9/t5vY/tmWz/0avO9+yX09rWKdkOd8LrtQiFw4dXEe3YrfHdvVDbWPvfA7Uvc2opa4fnNB7fVmG2xz8Q7sbsPzwrOX/Jxnb+I7rBcX/L3Wasv7fvQw1kAn5J0DABye8HqprXn9zZ55c8PPUCJy5Vrcxxry3rGX70LvX7+IaygG9QfxHmUgubXv7x9n/z/xLzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2QvuLRQ+UNFpIGHqPEKoJhFixkKDiwotLYV4UCLEtkoinHSz5GIyl+Ubas9vr6m9mkUBrbTkAlNIuX0igi2lEEJSc1SConkexVKVyIwSwFIi38w+JUKYUwjwUb0+ofU2CGGbceKCT+NAhOT+GkT87rZcLOpyG8d10/M5bMWcZ+IcnZKFS0TfjRCpron98pa3fXbJxY2uyRwBvq9zIai3FAJ3T4+jqNaDEy56txDC0bdCqOqqJntDxOUAoBWaR4nw9XUXJ78VIumvXnGRrqFjAoZcMG6EEgrna9IRAaarCx4Pd0Hli4GITY4i9hRCpPrwgPvBN7/+frDdCJ+5uuRiZZdXcU96IaqqZClTIdI+TfE8t6LvcRAC58Re11FgDQCev+b+lZVckPfohAhj59yPmIAwAExCbLkl8ThTIuQl9+mEPHOceK4cBh7LlFhZSQTpUiHSuAuZEHvOyNyk3ppIY6MQwKUdibaiPKA1RiHUr9uGDzAT9oQIvB4/5SLtqoa8/jSK/m3e8LzVj0KwXNU6ZZznJP4/0FDwPpiAJwBU5AwkHRcqnB0dU/vx40fBtr3gtezlq5fU3jX8mQM5L/NlzM1fBTc1d3hWwtQD971xxv2mFLXUATmP1QHvAx+8R831Kvre+pbn1CLj/puKvb3s4/iKXtT7B7yPx9/6TrCNJw9p25UQGz884mtyPUT/vbvk+Uhos2ISMbnIyDxFbh1FvZPk5J4iAmsn7imNqE+YKHehLrM7oOqPfopnthVC4YO4JzFRYIDXFDlZSwA4POLxICVrMSu475bCDnB7RnKGEgovCx6rO5JzK9F2Med1RnHGa6mT42g/JwLaALCc8fVrW17DVGU8A08fc2HaYxFXBiKg/IaI6QLAdsOFksuKn7kZudOV1VcviNw2fH0uSN378Sc8711cCKHklt/rWexgfg4Alahj33nnabAVos7eCpHqSrSvyLujVLRdiDTHRMHrLc8L6RXPc33P401VxjpoIfLWXNiLGa+lBlIY3wnfvRX2OyJW3Yiac1T3lKUooov4vjAZ+d7sQiL8sW/iO8j11Wvatl3x+7GKpdMQz0sjROTv7vi70JLEtXMRX+/qa2p/c83zGbvY1K3IF+L963we43ShYnfP9+D6htdGr8k7tts77qOpqGWFdjomdS/8Am3ZE1Wpo+LhX/HQYBrV5fce+JcaxhhjjDHGGGOMMcYYY4zZC/xRwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF/qhhjDHGGGOMMcYYY4wxxpi9IMrNC7q+538Yokp5ItTPk5R/Q5mIMj0A5GkWbCmxAcDy4Ijayyoq2R8endK2h4fH1H59+Yba13d3wTZfRhsA5OUFtbdtF2xszACQiblDrF9H9maSyvTcPoxchX5CHEuSir7F+NgntWkSfUwDNY+TaA7yh/Te7n5/BjEAYk/5UmJkYwXQib6TKdpz0cfUqf1j4+N9HBfc7+qS990R3ygr3sfDowW1Pz47DLaTY952Ej6qfJqdi0astfIvqBhHzDV5HgAMYs+K+Tz2O4rY2bTUPmYVtXek/bblZ2sX+iHGNQDo+mhPRV6o5iIO5twPTo6iz7z3zhPa9u7uhto/+eTTYLu44LE7GXhOTHMeZ7ZNbH99u6Fte5FvN3Xcv7rlbS+ubqn9wUO+308O4volBd+DXsTpUoy7JzEry/m+Z1lB7UAc9zDyufQ972MU7ZMkji9J1Ti+PCx2A0DfkXGJNU4yvm5pzmNsSuKgLANEwEvJXuU572RWlbzrnNtzUjfMKh4zZwt+tgZyFuttQ9s21zxmJglfP1q+iFpnECXabMHnni3jP5iR/AkACxLfAODs0VmwXXY8/l5f8Fp2FPE6JXtcHvK57MpnNzW1D03cgEnk9vlc+Nicn+VqGe1Jz9c5Gfi9YeyiP40j9z16zgHk6kyTHLhJ+BxT4TfZQezjfBZ9BgAOH/A96EQdeUnSVyvOXZqJoCNiTpnHM50VIiaK2Ic+rnenahMxDlWnsQtMLwvGL8+Hn3xM7dc3sYZJMr4Os/mS2hNxrwKZc5bx2Ltc8lqzqqL97OSEtp3PeB9Zwv2uquJYyhmvC98RNeC2jn46m/E4cbjg63ewjLU6AJwcxPbHh3x8A4kfAHBxcUntGTlHD875+49TkTM2m22wbdf87DNfAIDFgs99MY/zrEredhcur3gNf3G1DrY3om3T8FggyjSkpBj4ou+75suDYNvUcT8AoO94PT0rxd2oiOciFe891L1rNov5ZbnkvnsgzsXJCc+VVRnP19EJ991CnOdepJGB1Mt1w3PRTc33vSU5NC14bCpmfA/QihzaE6eS7y6+PGnG64OevGvsW74+mxWfQ7IUtR95f1KvRJ5teM6pKuYbPJ/lb/i9+cXrFW9PYuahiMdz8v4FALI8jm+9EXcMEVcuLvid/HoVc0A7cEevxHmeBp5HClJb5uLsq3eOtB4QcS8VdyN15xxIsFXx9z74lxrGGGOMMcYYY4wxxhhjjNkL/FHDGGOMMcYYY4wxxhhjjDF7gT9qGGOMMcYYY4wxxhhjjDFmL/BHDWOMMcYYY4wxxhhjjDHG7AX+qGGMMcYYY4wxxhhjjDHGmL2Ay6gTkolLl4/jGGyTkjlXiuZdz+15/Ac5UaAHgEwormd5nGJZlbTtwdEBtdf1htrbNirczxYD77uLbQGgbepgSzK+LalQvc8Kbi/7OJaW2ACgH+I+/uVohD2i9n2SXWekjy/WtxpeksW+04z7zi4cl/y7YJNFez/ytd+o9Rn5gWFLkYt1SAf+TPbIA/GJ88mS+9dSnLmWHPTFgq/92emc2g8Oq2BLxfjWwqcHMfeELOA48bVuxN5MCf/DlsTDXPR9V4u4l0bfbVY3tGndxfgBAPODUz6+N6+Dbdxs+Th2YBr53IaexEESowEgy+M6AEBRinNMzlee8Vj/cLmg9sPjuG4Xr17StldvXlH79fUtt69iHtm0fJ1SEdhuN3G/G5E/W+G71zd8fAnJrUuyHgAgjhwykStHEhPSgu9vLg76OMWHdgOfe9aLekXFQ9L39AVy330ZSM0AiFiV8HVIJ35eEpJzACBJSPwRMWkS9pHVByJZy/MplpPVbmPD12nq+BznZzFfFG+iDQC2dy0fiChU0oz4birWKRX1VcLnw/pW/9Wo73icXt9dxXGAP69a8DVBNqPmkydnwXb6hMeEXblb8fm1TVyQlORIAOhFzdSRvPy2n2iv2J4ASMXGJAVZ05GPbyJ3GgAYRH27Joem6cT4RLxgcSEtRNuU+8co/HfbkriZijtaJu5oYr1bcsZUPu9G3ndP8msr7mIq9uUiT7E0NQn/24X54TG1d2S8szmva87P+ZnNxdqzWJgI/1LxPid3sIODQ9o2YzkKwNjxWH1+Fvv51re+TdvO5iIP1Otg69W7iC9wFwNEDUNqDAB48eya2p89f8aH0se9aZuGtt0+ekjtzNe7jvdRCv8vK76uFbGXJc8vu3B7x2vNuo4+03V87dW7CXZHBHgJk4q2ZcnX5+Aw5lT1Km4SL83Ue7Cc3KXUXDJRZ1fkPOcp379DcY8az06ona5fzsehYnrd8pjQkhxfi4vKduTP3NB8y2PCNKl8K/aSdKPqkp0QtRFIjE3Fu8NE+F0n7i89yam3G75Pq624s5GxjOKdHburAkA/iHsNOQNFyWsJdbbWTfSl7ctYewOA2AGM4r3I8ugk2I5FJ73w/6sbHr/bhj1T1ZD8mSxWJKJeSlUnon6gd3LR933wLzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qv8UcMYY4wxxhhjjDHGGGOMMXtBlJtXDTPelOiWA1L8XHxDEarowzgG29Rx5fdmpCNBXdfB1rXRBgCDUJUfhVL8mMTxVYsZbfug4Ou32W6CrR+EqnxWUDvE3Lsuqt43Yo5tx/sYhAp9R9r3Q1wPAGiFfZqiowyjWGsxjlF8l0uFD37V5AV/flnEAeR8iTGJOacJn0RCzovoAlOv1p78A7HGSzHHQoyvJ+elmHPfXZa876SPvtsTfwaAQfiu8oGTWTyLF1s+jr4X/i/WuyG+PoCP+3YTzz4AdA2JWZsVb7u64uO4u6P2vonPrLKStt2FPMuoPcvjOmciLWQZ38Cy5LG07+LadwNf+yrncz4+PQ62xazifYiYXtcdtb+5jnsybba0bZnz89L394+7U8vHcXHNfeaW+Mzpk/do20LEXbXeU98EWzLxcYPkBQBISLzJc74HqtQYxFoNZNzJX0MSyVN+LtKc2fnzxarJPMJgawkAg6olWC028baiazFHYMziuOst73vbcZ/GLG744tGcNt2soi8CwLARfadxLGMqdiHhcxzUoiTR78Ze7E3LY8U0kZpOuMLyfEntixNuf/DOebCdnh7yzndkS2IbAExk7aZR7BXJAQAwkj0EeC4Z1OKJnMbiRCbaqvo2S9Uzo18Xoq08/Un8yyDidyb8ehB1GorY9yjuiuKaBzXyjNSjneikaXneaYm9H7gvqPBZihyTjWTfJ77vu/C7f+e3+R/IsJaLBW2qaqZ6y+vE2+vrYLu7u6VtN+SODQCbVaw1r655W5D8AgBTz8/50TKu82LJa7rHjx5Qe9efBFsv3g30jXg3IHJgSmrqXuSuO7JOALBY8jjbkPUexD2lqfm4Z/PoJ8sjXnOquqKqePuUxL5UnKFduLnj+XC1iuvTiFp4VDWoguQiVUsVBffHZRXvE6UoWCeRR3JyjwKAnOSLVNWcYtwZyWe5qN0SUe8kEHYS03tRc7YNr9NuVRxid6OE+91q4HO/Jcdl03Df6cV7h0nlObIP019DvlA3BFYzyVey4lLedHxPtuTM3ax52414J5WQHJBmvO1iyWv705MDamfvu7KCx69Ny/f1zU30u1sxxyLn/vX49Ijanz48DbbDJX/vUIurxCjyS01qIPnOUb68ZzZ1T+ddpPI+TTsXbf/V+JcaxhhjjDHGGGOMMcYYY4zZC/xRwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF91ZvUhofRCsNk5KsE+Zx5CJvIxHv7FohVLNdczsR45XCTkoIUMx9sYjiiiURgQKATIjnrddR7EoJrylxl0mIs45EcLwR69cKEWYmtAcAm20c46bmfSdCAJJptgq9OCmGq8StmdbsX4ckk1I5ZgLieS9ExcW5qITYUEYcchDiS9kXEEIrxVwqIRSeEHFWAOjYM4Xoc67UGUlM6IQYnprjUnyyfUhEyy83Qnx64v6fiGUdSEBUgmyTEHbaXL6JfYjg2a5vqL0SwpAzMvfj8zPadheqORfymhMRdJUX8owLeSVSROr+glMDiY0A0JFYlYrYfXQcxb0A4PSc56KXl1Gg++6OC8DL80zOqBLLToQK3HrNn/n6xbNge/eDb9C2Bwdc8GwrzlxDRP86IWiXiljBEnFecTFGJWyYiTWhoZkI7O7KbM7rA5ZHWP4GgGZQope8PbNPSphV1XmI52JS+yf6mMS+TkzIOBfnUwg+J8QNlo+5+HWvzj5TiRT0av1EPmP1AADkRMiyb4RYsxAw7IZYQxY5PxeH51xI8fwxj2Xz5SzYWF3+VVBUIngQMc1R1YNCdFSpd2ZMRFX4by8cOyVnV4mNq2psUELhpJ9B1XSii46cr1bU5Imsd/gzc/LQUfQhtG2peOzbfqKtEwFqEP4wEF8dhFB4Iu5/7I6r2ifKL3fg2994j9pHxLmt1lx0+uLigto/f/YZtb9+8zrY1N17EGeOCekeHfKYfLDkAufLIx7HHjw8jm0XohYo+L6WZawvp4rXUkPFc4M6i3ke46ZiMedr8vTJU2pnorf6f6cKQVhWW4sY2dRckLsTNSrS2LeqRXfh1etYTwNAU8f6qGn5/k3yfixiPW2qBLe5LzHhXvXeIxdC3LlYT5aiki8ohs6Os7wHixq5EMG+yKNvpGLyGak5/yo7W+2NuGOvt9x+u4l91+L9pPYdsTfEH6b2q79jjL3wdXJvSISP5iW/p4w177tpon1b83Ujx/Mvie3zlK9xKUS+T454fcveTdYNH58S/359E+/NtyseG2nxAqDZ8L4nsn7tCX+HUopasRPvjdmBllr28tVK/IPy3EHUc6r6zdhD1UX0HviXGsYYY4wxxhhjjDHGGGOM2Qv8UcMYY4wxxhhjjDHGGGOMMXuBP2oYY4wxxhhjjDHGGGOMMWYv8EcNY4wxxhhjjDHGGGOMMcbsBf6oYYwxxhhjjDHGGGOMMcaYvSC/d8uMf/9IiUq50j6fhPr5NHAV+rqJau7NdkPbbtZ31N4QRfjlYkHbzqsZtRfpktqxjDOdVVyxfhKK8NMY7W3b0LbDxNcPKd+bjNiSqqJtq7Lkz5zxcbP2Wb7mfdzy/R3baBczRDoNvG/hbGxdu7ETvX958pKtMpCS8zIv+T6dFryPYl5Qe5nH9qNaOOF3WRbPbSnGsZzxcaQZbz+QZ6qzPyZxHACfzyA+wfYdn2NB5ggAFbHPct55peKeGMuC9FOVfP0OK97J5WefBVue8rlkiVjXw0Nqz4lPleLs70Ka8TknabQn4HPrhVOPHT/HXdMG2yD6UPG4Z7mIxBIASIT/P3rymNqvbm+DbUvGDABNzXNASVJ2WfKYnovxrTc8h/78w18G2/mT92jbDz74gNqbmueA7Sbm50HEdAk5LvOR5/JqznM59zRgHOI+jCrf7kAu4gwb2cS3DxPN7EA38PEmxNdTkTeThI8vJzknUZ3gi+3rQHJAIeJuwcMKQPJwQcYMANm7vJ4bHyifIXsj1qkXBckoYgjI8d/e8JjQ3vGYMPVkvXO+B7MDvoCLQ163svomE33vSipy2ZTEmMx8+m0fos5Oed8DeaaK6xB9tyRnTKJtr+qxka8pq7ESETfbgUe3AXF8TSvqaVEjj2J8GTnrifi/crk4M6NYk4rsTZbyK2uh9qyIz0xEuaNqlkzU+Kx2VfXsLpRkDgCw2sS77fXVK9r2+vKKdz7yu9kTUsMcHvL4OD/gsaPI43ou1f1TBPZMvE04Pon1rTq3dyv+boD5TCVqqTTnfpeL/c5y4mSibZrxvg+Pjqg9SeO4h17VxOo8R9s0cT/rFgfUXovate1ZrBXncwc2a/78gcTjUb0oEMhjzOyqsTBvN9tgq8l7KkDXGY24A+VF9KVUjE+9d6NmVQuL+JGLu/diEWNFJs4WmQoA4HAp7jtkSdY3/D6yFut9RWqstXDdMRWxQvhaMsW1SmUN/eUZGn6/6+toH5voiwCQqPcyMofHOoDZAGAauL0n9lG8oh5HcY9S7wxI3+w9FaDviHPmkHPuAwOryaHP3O1NzFEzUrcBwJF6LygGnpMZFaJtKt4zleTeqjy3E3cddZtmdhWz7oN/qWGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qu4tDyhyDJqH4jC/TBxnXOmbg8A4yiU4seOtI02AMiEUvxiFqd4sOSK9fOKz7EWz2zbNtiamo9jGPiarO6uo211S9smCd+uPOPfpvK0CDbRVErZ52LfkcS+p4mva9uW1D4Sf1D7OyV8GJj4wLsh7sNIfHVXZgVfn0PiS0nKJ1EK+1DydZvInnQj96+sFxvLFpQ/DqNY42Tg9pS0H4SDpQkfdzpFR81G3kcmfCMX6zqSsaj1m8Tcs5QfpKNZPBcPjpa07aLi5+XZx58FW73Z8ufN+abNxblNu0PSd0Pb7kIv9oq5zCjyxVTX1C6WHiOJsWr/epVzSPtJna2E972Y83195913gu3ihsf6tr+gdjYSccIxJdz/2y7mLQB4+eZVsP3FX/yEj2PkfXQd96XtZhONIhml4twC0d60/FxkK54rJ7Hv4xjzRZrduzy6N/WG+3RCglgiHH0QcRfizCVk3VTeLFIeN/KS1BI5H1/X8zkqv2PjTtSZE8/ss1g3JML/y0LEBJXniC0VvhtX6S1dx/2uJXXrlPG2aSbGR+ZZzvg+LpY8Xxwdzag9I/VNnn/15wIABuEfwxjXWsX1MeH7MvbcF5Iuzi8VEbUX9WNL9rYTbVUFqkJNxuaTiHGQmgkAWhLzulrE70Hsgbi/JGRGiSjWy5z7ZCJqlYbExFJcRxbiPJbMV0V+SUTsU+2Zl4wi5uyCyuMZmfPx8QFte3zE7YvlgtpPTs+C7fCA1zVlKXItiZxtTeoAAOtbXgfdXF9R++3tZbCx+gUA2ob79OnxSbC98+QJbbuYz6k9FT4z9DEfqZil6stUnAuWG9XddhKxjJUKqo5U/q/sLDcmIi7vgrw3yImQtsKu7ndlETO88v+iEBdq8tDNltdMmUgMjagnij7aMxE/RvKO5O34Yh+TaDv0/GyNIo9sNnE+pXjPoepvNR8Qf2jEHXLD7iMAtts4z17FN1GPTR1/Zt+Q+6l6P7MDzSrGRgCob14EW7vlcXckNRcAoOd+wK7C6r3WQtQ6DQlK6lxc3fF75rWws0gxE/f0udhv9oLsQBUk6n2SOEc5mfvU8rlMKT/785T70kER93LT8L0R1yv6Lk1VOuRxAIBOvaslSzXI3v/V+JcaxhhjjDHGGGOMMcYYY4zZC/xRwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF91b8U6KVCRGFSrlGCiYhBDgOXBwaRFRF6DIjX3Ahr6KIU5wLgV4m/A0ANzdcrOzqMgryKHGo2YyLBTdNFINRwsyzim/XrBQij0TEjAmYAVo0Sgn+pkQZqCy5j8yIyCgAbIkIkBJpTISgdC/EeiciVDUoP9sFIVCYEVFEJXSdi7OlBJET0k8nxGNH8cyJtG+YYg8Aoe+IVAgTMd9QskJKFHgk7RMxjkwI6pVCQbwsox8owfdKKHtWJK4AwAcPj4Lt2+8/pG3PSVsA2JCY8Pryjra9Ef63FDGuvolxqCbP25WmFWKmRMhuGHjbBnxcTCATAFU/zJTIoRCbS0j8GYW4dCLiiZpPmsc4eHB4TNtu1lwAm4n4peKAKqHJPOG+kZP2nRAru7rgopydyC91Q2K9EByW+0sYb3nMGkQOHZRQG4kV8yXP2buwrcVZI6KDmRJkFkK8SrSUCYUr0VIlrp2R4Ku2qRY5Z1Nzn2Yi2hOJEwDQCWHKgQhuK1nmXtgHITxK10S6qKgHhIJ4Osa+i6XoI+O1ZUXsywUXAD46EfZDbq/mceCpmsyOqH1hIrhKXDdRZ0bUAj0R1546IWgoxHh7cq+pVZ3NR4dBKzTGPpTg4sD76IgY6djzODQIgddRCISOZB+yivtHKcSCVf3bkr3Mcr6PG3F2Z8w+fDERykLE25GIp+et2uEvjxI+Zufw6JjXE7nwr1zVDkSMvmnWtG0vzkuaxP0bOn4umpYLwtbCznLJest9uu247y6X8d48iFpP1RPKk3jMEjWJ6FvowWKa4nwmUXP2wh2J66IXubWXuVgMkOTA9K9BKFyJoLMUzGqgt3YOe68F8JpV1dnqXjqRLCCutijEONR8RnKvH4m/vG3L95uFClXrpUQ4HQA68cyG3Cda8V5wEj4ziDr3ehP7ubrhQti3pO3b8cX1e3PN69Z1x+PhZuBxqN/E9n8d/6u82wqh8Nt4Z2u2K9o2q/j7VHVeZsQNTg54LZYPvI5tWrKvI1/7Nxd8jbdbLgC/XMQ778mSj6MX+T4j7yB7sYFFyudepPyZJckXC1GzLoUSd9Lzs3g6j2O5EblSkZA6alTvakUfmfgLK8cS9d7mHviXGsYYY4wxxhhjjDHGGGOM2Qv8UcMYY4wxxhhjjDHGGGOMMXuBP2oYY4wxxhhjjDHGGGOMMWYv8EcNY4wxxhhjjDHGGGOMMcbsBf6oYYwxxhhjjDHGGGOMMcaYvYBLtLOGSnE9iYr1w8BV0UEU1AFg6Fpu76OdKdADQF5x5fcij+NOiNI8AGxWd9R+8foVta/Xq2A7O39A285mXPUeaRzfbD7nfVQVtRc5nzvIPtQt35u27qh9HPhaZUT5vsy4jyznfHxNE+1yfzPuqt3E55MQX6sbPpddqGZ8XENLni/ORT5yey/syUDWSPTdiyl3ZF8TcWzTie/rrOI+TUIC0jShbSfRd5LGThIxwDLjfY8Ttz8+XgRbD972YcP98fAg9gEA3/nG42B7971HtG0izu3VmxiHtgseE1Lh/5uup/bh+ibY1tuGtt2FYeTr1hGHHAY+1kTsySDyCMhapCPvIxf+yPqYxBp3LT9c22ZL7U0b81kixjFfLql9JGs1iXyWZuQgApgX3HcPDw+D7fT0hLaF6LtvuC8NZN9b4aPTyOfT9TFHdSwWAuhF3hpH/syc1AlLMZddSEWOZK6eCN8VYRoJqSXePpPEUnG2VL7vydr34hjWa+7/2zVfT7ZXmRjfJB7KWmdirRNRLmUiX4D0k+bc/5GouCL2poj1Q1HyvJpN/JmzLNaFVSr6KHkfrYghsyzmnaqc0ba7opaU1ciT2KtExOoM3G/SJMaDXsQUVSBNKck7oo5V52sSTsnq20HMvSX5BQBYeu3J3QoAEvD4OA78foCE3K9EThtYYfhXwEqIuubjY/cRAGjS+9f86i6hateUXJ/njTj/O5CKubH6O8v5HVGkBvRiX1tyJ2e1GwDMSnX3jmtRVLyOPRRxs5qJWEPadyI3KF9fzmPfc1FnT2IP1Pol5P+LqvuViDaYRAxhNWrf83PRdnzP2DuathG+0PK+1f00I/eakvjCzsh7wL2N4JUDMIn7C6vhx5HHtbrm9c5mW5Nh8D6ynOfxlNyP346F+QbfV3Xvmgp2gRcxUNWchXjfNcT2rK4HgEb4bi3GfbOOe8NsAFBvuf3uOtaul2/inRkA7m54ndt2fD7TEP0hF/u4C9Owofauvgq2dnVL2+Ydj4OJuPelZG6zhK/xmPOzVZD4XYsYOE+4bxwX3E/PjmJePD/iueV6Tc4ngJ6MpRLPO5jxnHgo3hcekuK3FAljpp4p7O9fxD2+2/C92Yoz15KcM4oYOai7pZiPuot+WfxLDWOMMcYYY4wxxhhjjDHG7AX+qGGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXsCl2FnDgqu5J4iK5tPEVdHThKucTyNXXO/7qNA+Th3vg3eBniirb7cNbXt1dSPGwZ95fnYWbO++8y5tuzw4pPa79TbYVncr2jbN+HZlqVCbJ4r1Rca/Yw1ZRu2zlNtRxWemYhyzakbtZVEFW9f3tO1iPqf2YeTPfD1/E2xvLi9o210oD5bU3tfRVrRxPwAgBT8vo1jPnmxJkvJ9bcVny2GIz+z58NAO/A+FsM+K+NAsFwMRfteR761Jwp83irmXImadEd9dHi/4OCbe92zJ9/3J0wfBdnTKz343ivmQWFbkfC5Tz2NZNeOxgsWylsTZXelVTB/i+R4G3jYV39zTSeQRkgQmlXPE2UpIzBxGfj7rhueFpiaHH0BH2vcdj3ezeYyNADCO0Q9GsX6ZyBcHwndPTk6CbXF4RNuStPp2LCKG9OQPfcfXrx+4ve2in7a9qh34ug4il2csLwrf2YXljO9rR3LAKPycnSEAGEWIncjcRrGBrVifPiXjE3mr7ri9FwlmJDWkqmkykS9ykgOSjPeRl6Km6UVMYGMRfUPkomng7QvyzHHB+0iEP+ZJjAl9zffgtr6j9u6Wn6Mmib52evTVnwsAGGqey8Ys+seoAo1Yo54VTQCyqYx9kxwAAJ3Y2wFx7fpMxTBhJ30AQD/FcY+jqJlEzh1y0l6co0Tc3bIirhMATEXsJxFtoe4evDW6hNSoIvZlIkf3Sex9FHUkq4kBIBUDTIY4n6ETsWUH2pafi8Ojg2Cr5mKfxLqJMIthiLk2TXnfZcHvZkUZc11V8rvgYinqDJIbAJGvhe8O7DIGAOScq7jS9+KOJvLlxHJazuuxRDxzEj7NwpMadyLyPLurp+KOlqi8qGIwiUMJOcu7MqkilKy9yp3KLmYGkD0ZxR1R3YE6soGsPgaAbcv9qxB3D/ZMVWcP6o5GxleK2jIXMV3tDJvntuPjuN3wuLfq+FpdbWKMW/EuUPNwiM0qvo+7e/OKt73aUPso7h6Yon0k+X1XmtUltW8u4zux7XpN2y6WPGYWpcgvTcwXQ83Xp1lx+0DuDYm4Cx5WfN3KhOeXh2fxzntwxNs2DXeaNTkv84rH9AcnvO9jcf/LSVzJSe0C6Jf2R0ve97snce53a/7O5/WW26+bOJauFTlRRk/xPofYdrl6+5caxhhjjDHGGGOMMcYYY4zZC/xRwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF/qhhjDHGGGOMMcYYY4wxxpi9QAmpx4aZUC6fogr9NPFus6Lgnadcyb5p6mjb3NK2db2l9s1qE2x3qzVt2/dcbf7x+QNqf/9rHwTbu+++S9sOA++76fpg22z5XEYxvkzsDaaoTv/FdOmBYlZS+7yaxbYF3/e27ai9qmLfdd3ytmVF7ZMY+TTGuav93YVJ+G5eRV8vWv78ceDrM4rN6ol9ShI+voSvT0d8o26iDQC2PR9fzc3oh7gmsxk/+0nBx92SOQ4pbzsWfA+KjD/zaBn9Lin5Og2jeGbKfb3I4lgy8D7Sivt0ehLtRcbPYbvlsSzL+DOHKcabLOfrtwt9x88xc9MkFWd44AdgJGcbABIa3XgfxP3/su/YXjRFIvyRThIASJyezRe0aSHOS0b8qxPxNRfrOp/PqX25iHb2PABoxf4OYmFTslYFif8AkJOaAgCyMp65jORPQK/J0PO+E7JnZRlz3K7MRQ2UpnHdWj419OJcKJ+evkC+SES8Y2dxYB1D5+RMxLAsiXmxUGeItH370PvXOmKKSHJR45IcOomCaRSxfhJ2VrtliYjHKsdP0T6lfPaDOC9bUXOyc5GLmmdX6tWK2ruczE/UcomoQbOen7s8I/FexM1B2Psxrmkz8fG1Hd+XLuH7MghfZYzCsRNSOycFj205+DhSMfeRmBPhvyq2DCKe9cSvu1HU0KwoBjCxHC3uS6OKiSKSkG1H0/C8uAuvXr/kzx9jjjvNTngnMp6KOZM9zMS5z0SdzeJ9mqv8J+pssd9I4p4IF0UC/sxR3MkZqcg7o7CztcrFOg2ij77nZ5HdbRNxz8vF3YjG9Vzdafg9pRPjYwVHnt37VdP9UQme5Fo2X9H0r2zP9rUU7yYWyyW1l7NY84uVRC9eAqxFnCnHOD7l56OKawmpRUU+K0XNpN5JNeQdyN06vucDgOuVsNd8te7qOO7Vmt8DxKsO+v6pvb2jbfvba94JVH10//vpLjQbPt6B3NkScbuduob3wZIegKGN/XQb3sfmlteazMVScUDZOxwAwJLHmdPDWO+U5B0dAMwK7rsVMR+I91cH5K4KAJnY74TcX1LR9ov+EmFGxnI853OvxbuVdU/uVyJGDiJmqTqKxdpMxet74F9qGGOMMcYYY4wxxhhjjDFmL/BHDWOMMcYYY4wxxhhjjDHG7AX+qGGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX3Fu9aSCiZADQExEiJYo9CrEhJdY0EsGprRBIuri8pvbVXRQf7IRYYilEPGdC8On87DzYCiKgDQDdhgv6MmGiessFdrqWzz0TAl9M142JXgJAIfpQQnIpERfOxfop8cyqjPNU4qNKrFcJXzIBptlCCNXvwJUQ5GQCdxshlHMnRA6JNg8AoCFCXkr8VAliDWTZiM4WACATe6LEw9iOLJSQnRKmJUJjqRAqLITfKQG32TKe0UwIhfdi3L1S8GQC4qkSUuTjY0wLIVwnhNqGnouspV1sP5Rf/bmAEtA6ILFUCYuKrpWIHxWKVGLGUlX5XiYAf4WIpXgmi71KUE8JPDJR1UGI5iqxrVSKIBIx71KIeQuR71T448TisTifUjSU7FkjcqIS2Rx6UccQu5rjLlQtF4bHnOR8Ih4OABCxKlEC3UzVWqiqinCHjJSKoxhfWvA1LsU5Z9ZSnLpe1aFdtPdCab1Xgtsi4Axkmkw4HRDCxOC1LMAFmNU+phlfv4n1UfDn5ULEsij4WSxIblBz35Wi5wKXUxLz9SjECDPhwCkRFgaAlOUBcexUvM9IHZQl3E9T1YfQl85JfcnEuQFgEH6TTLHzXIgnF6KeVdr1E1tXVlwCmDohSsoOGICMjCUR5ygR8Z7mOvU8kc5TIm4LgG5ElfK72y58+vw5ta/uroPt5vaMtl0ezKm9FLVfxXKfOPaDqoPInqh7nP6L8BlybsmVFIAWm02IncVSAMhE5+pOwgSlVfxW50XFOJaoElFHpol4N0DOxZCJs5WKmNWLdwakG73vX55c5UMSIxKlIi9IhLB5Rerh5YLXdDNxL726vAm2xeINbVvMeF6u50IovGDj5quvhMJz4kuT6CNT12Cx3HUb4+Pdmr8buxUC4rdbPvcNSblbISq+Ei9GhiaKWCektgSAVNSiI8m3AL/XZOWXF0RWdBP33Zy8x0zEez8VS+WtnNSE+UzknDlfexaP1fiW6i6R8Tvb/CDWkKrv02P+vpfNfFnxPlReTcUlg9b85D0fAPQiL3QiD09kntWcr1Mlavs5qd1y8YKyl/cD5eukvYXCjTHGGGOMMcYYY4wxxhjz/+/4o4YxxhhjjDHGGGOMMcYYY/YCf9QwxhhjjDHGGGOMMcYYY8xe4I8axhhjjDHGGGOMMcYYY4zZC/xRwxhjjDHGGGOMMcYYY4wxe0EyTZOSKjfGGGOMMcYYY4wxxhhjjPn/GfxLDWOMMcYYY4wxxhhjjDHG7AX+qGGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qv8UcMYY4wxxhhjjDHGGGOMMXuBP2oYY4wxxhhjjDHGGGOMMWYv8EcNY4wxxhhjjDHGGGOMMcbsBf6oYYwxxhhjjDHGGGOMMcaYvcAfNYwxxhhjjDHGGGOMMcYYsxf4o4YxxhhjjDHGGGOMMcYYY/aC/L4N//3/++/zP6RFNGUJf1gqHpfw9uM0Bds09rxt11F7P7J+xbcc8ry34+Pt8zzOZxRzyfOMPzKN7euODBrAIPpOSB8AkGZk3GKKauqpWKqc/CHPeeNZwefO3GQcB9q2E/ubga9VTtZkGlra9r//279K7ffhYFZRez/Gcak1FmbJhDi3VGxUJs7cYrkMtqcP36Nt33nv29T+9DG3n5ydB9vBjI/j4cMz/sx3HgbbKM7h7d2a2l+/ekXtr158HGwvXjyjbV++/pzbX77gfb+O9qbe0ra9imXUd76YlyTCq1JyLjIRP1bb5gs987/O9d2GP1/EMAZbBwAYOrFuQ4wdA7EBQJrymMRiuspPCuGmSEg/qmv1RJYT5Tr1fO7jwNtzj+FWttYA0Is4XdfR3rV8H+stj9NdE+3MBgCbpqb2ZuQrmxUxjueLWNsAwL/19/81ar8P/5c/5PGkGaM/Nqx4AdCPfE+2DV/7TR3X4vqWx8yPfvlzav/wT/5lsF1+ytvWt5fUngo/XRwcENuCtp1EfTD1cU1UrMlELE1ELTGSOL2+XdG2d3fX1N533E/zPPodW4+3bXlgGfrY9zBwX5jEHuQZz89sDaeE9/2TP/hDar8v//yPfkntCanXWRwEgHEQUUycGZZXZwU/92nC+1ivoi/kIr+cnp1Q++Gx2nNW6wm//gL3AJaL/vIPog+VkaK9E3GrqfnZ3TaiPemnn0SuEzltIHeprchRraorhK+xdU1zPsd/8KP3qf0+/K/+b/zu3Qxk7QdVU/LYMU7i7k1sHYkzADCJPZlVMbYl7E4KYJDnk5pp/TaI2KbulKw+0nGFr6uaO7t/yvMp6kV15NgRFWFPzn1ktaF4XiHu74V4p5Em0Z51/A707/13/03+0HvwP/8P/ojapynmMhW72XsMAACZA8CrYfX+JRG+1JPNUveUL/zChjgH91AgEw5GpyNfXqjeVY11/3usvvMqZ2dz53McRI4aSRzSMUjsu8ihExlfjxva9n/2P/z7/KH34H/6v/x3+PMR8570DXGJHWT9HftW/l+JWrMsy2DrxJ2mbXg8yURMWh7Mgy3PeZ1Xt/y9R83qBhU/RF4dep5HepJfVC5S8UblFyDuWS/ijXpnwN65yHGI+rmoZtReFsQfmugLAPC/+Pf+19T+X8e/1DDGGGOMMcYYY4wxxhhjzF7gjxrGGGOMMcYYY4wxxhhjjNkL/FHDGGOMMcYYY4wxxhhjjDF7gT9qGGOMMcYYY4wxxhhjjDFmL/BHDWOMMcYYY4wxxhhjjDHG7AVchp4wn0f1eAAYyXeRBFy1PRdK8UPC7QXpZxx43/04Uvs0RXuWcdX2USjWj/yRyNPYd5JnvG3B59iTvjM+FfCegbwUavNEVV6q3pN1AoAy52vCFiVP1b7zvpk/1GJ8ydTzYYzc3pBuuq6mbXchBV+fMotr3w3CR0Xf48T/kibxmVU1o20PD06o/Vvf+kGw/fAHf4u2/do3vkPtj548ovajgxgrhpav/eGyovbz84NoFHGiqVtqf/romNq7774fbK3o4/r6htr//M//jNr/y3/2T4Pts+cf07brzS21t2StRhGEWHz7q0iIv6r4thOiTzqPRDQWc1Pngh1FdlYAIElFXBPmL9J2EuOb2KKI7VPDE0+kVnFcZMyi4xadJGpd+SMxkdyaiL6HnueAsY99DCI/JZ0Yh4jBPbHn41f/fz6mhOdqVtaIJUam8izJ9wBQjtF+uCxp26rkdnYUk4y3zUqei9A21JyksbLJRd9JwvP9MER7nvP1SIXfjT13GlZiiDIFfc/9q2v5uJHEMYpSFnnOcyWL6TJEiiKyKLhfTiRet+0Xyzn3JSc1EwAMpEhWcT0VxbM8ymR+MjeIONvUMV/Xwg+OjkldA/2/y1iNnIrx5TnvJWX3rvuX9W/7kPGe1BPifGVikqWYDzsxozh3qoRgC1tk4hCIPpj/qfapuEPugvJHNueSxBNAx6tOHGW2hSyHv7WLPpLYuaw9SFtA38mZL2Ui/00Tt49kUWiNBiBLeD5STpOT85KpfRQ9q/qX3eHbnucXVQMmJCaomq4qxbqKgbNaprv/q6Z7k+d8T8YhDiwVASIX9eMo3lWx7VbnU1/NYifilZS83w3iQLM9UXXkF8lz6v6p1lWiBsOaqq5VHGIxS+2BXG9iUzFLZG3mf2/HEjvf1uqt3pcny3ksYDEiTfm5zESylrm6I3ZRyFYVf5e8mMV7w0VzQdte3/B3Jyoed32su2ZLPo6m4/eAbUPeEZG7C6DvGGp8EzvPwkfl/UXVXSwXCd/tBz539n44GUUNLt9ocr9krUeIC/w98C81jDHGGGOMMcYYY4wxxhizF/ijhjHGGGOMMcYYY4wxxhhj9gJ/1DDGGGOMMcYYY4wxxhhjzF7gjxrGGGOMMcYYY4wxxhhjjNkL7q3eVJVcEGUgYiFMtBH4KwTEhSIQE67qhLLZKJ7JxHkS8TwpxitEmRoiLpr2/DtRNwhRK6IGw8T3ACATQopzrvGIijRnej4AMAhRn1aI5rSbdbAlQoCvEyKUjKtbLgC02dxRe54IBTwi9NW1W/HU37zP0CiZEBRj4lJKVKsXSnuZWM+DgyiA/Y1v/gpt+93v/zq1//DX/kawPX30AW0L4XdKVLYnglTr9Ya2Xd1eU/vd1etgy4XK6SSEc1OxfmUZ/fHowSlt++jxQ2p/8s4Tan/wOIqQ//nPfk7b/vGPf4/aP/rlT4KtroXvKk0mIchGhdO+qNjbPVCCuQwlzqhU3vSUyVlUn+3V+hC7FEn9gmKrXEBc5CIlvkmE2pRwGBWIhc5zI8kBX9C9pIgtE9VL1HkWQoAsRXVC+FuJhhZKfJ4JZ371xwKj9On4fL1PIucJe0dyeNvweNKJPJtMMaYvDg5p21IIFvfrG2pnItUqtyRC9BVZnPtiwYUAR6YoCWB1u6L2gcyd1W0A0LWiXmqJyCD4HhcFFwSviJAiIMR0hYChqilU7TYMcdxKGHhXJlGD8rjJycShVZqoVKRw4udo7Lm9JwKSXcP9oO/4PUWNj+UeJeirYHNUPahcJ0W0if8qoXs5R6XJy/pWytaqBiRmJdislMwzJdjMxvcF6p77Ugkt2ZQVN+KsCO1aFCIWDkS4u1f3UlWrZKSeEGuZqrMvCjgmOC79S+wr0zlVosCq71w4b07+gTq3o6hhOnW1ZaKtShFZ3S3Z+NRBVOOGEnePz5Q1/g5k4vlsvDK3jHzO9C4BrlHNYiCgBXMzogo+qXcDvVo3FWfiuHXu430kLP/JuvWLKXF/EauCCdEDvJbSIt/qnSN5X6j6UFsjznNL6oTtLa+3d0GJyLPaQ+ZeschJIYSxSUdZxmv42XxJ7fNZbF9V/N2hvGOLc96wuqtpaNtWzL1pYx/DKN51q3yRK2Hx+/ehYgVJ2W/tJP/J1yLqLFIdeCHKTtYJACZRV3ddHM0k3qPfB/9SwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF/qhhjDHGGGOMMcYYY4wxxpi9wB81jDHGGGOMMcYYY4wxxhizF+T3bThNSuU9fhdJhMo5wO0puCL8OLG++ThGoUIPRIX2ifQLAJmQmx/RUfvQNdHW87nk4NL0SVYGW0HU6gE992qM4wCAdIjjbjYb2vbN5Rtqf/2a228uXsZxVHEuAHD28BEfX14F2/PXsV8A2Ny9pvai4uudEb8cOuUjX55e+HpCvhdOxBcBIBV+t5gvqf1Hv/6bwfbbf/fv0bYPH35A7QfLw2CrGz6XbcP9q27UXl0H2+qG+1G7XVF7NrbBVmZ8nY5PTqj95PwBtR8cxLnnxQ1tmxbRRwFgGPl5Pn8Sn/mb56e07dHDE2pf/ZOrYHvx+ce0bdfxcbC4B0BEoa+ebc1jZpLGPSSmt20TPodpuv8skon3IR5Jx5LkPE2SEPO2D9F7MqmnMvgc2WxU/ICwq3Xl7dUe8C4g5pgW8R/kYtjlxNe77aI9Ffm2xIzax4Sv60jGnah13YF+ELXRGNdH1VEDyesA0HcxZgJA326Drd3e0bYZeKw/XMbcPi94fupqvm6rgdceIHXNOPI5qrNVFPEwimOL9ZbPcb3ha9KS/NeR2g8A+i9oH8e4xxuxN3l173IdiTi3yqWHltdGbO5Dy/1sV7qBj6HryDkQAShPRY0lnjmQc5elYp1Fzp+Iryr/1fH0/nYVe3XeIX3IDPjF4v1AlkSlZ+V7GQ/hYDlw6LmPpGLf2VUvTXlcnYgvAMAo82W0D71KjF+eg6qg9nGMCzqyDQEwiGGNovgayInpWrFu4pkpWx9xhpRdlUxsD5Nc3L3FvWHK4jkfSDwG9LsB1XfO3juIszWIw6WeOZD3JfrdBWdKybsVVUaStgCQK98hNU4vztYusLsEAGTMd8XzO7FCmagT2VYN4nANynfZ+x3RVt11euGnSKI9FflM5RzmjwnpFwBSMT5VO09kb2RbcfiVJ7FcrnwkSXmsYO8z1flUcU/FkGYb69/6jr//2IWx57VH18S6Tb3XVXQl96WiiDmqKnncGHq+Ph2pQUVJSJ8HAGnC7WUR7y+JeO8M9b6X7HfT8AGqWF+JXF6olyCELBfvh9XJ6GPfcg/E/ZQdc1VrjKKPQQTElOaiL/97C/9SwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF/qhhjDHGGGOMMcYYY4wxxpi9wB81jDHGGGOMMcYYY4wxxhizF3Ape8Lt3S21F2lUkE8TrsI+iG8oScqHMSGqpfdtw9uOXIU+I+PLhNI8EWF/a8+5Yn2C2HciOsnKitorYk8zPr5N3VH7asX35ur6Itg+/PgXtO1nH3/E+769ofZuvQ62xdEBbfudH/6I2o/PHsd+2xVtu635HIGSWnviO23D128XxnGkduYGqfC7IuP+9c6jp9T+g+/9MNgODk5p27bh43u9jevctXx8XcvP1ra+5O2bTbBNLV/7lOwTAKRJPFtIeNtJxRXRN5vPelvTtm1/R+3bnq/JOMVnVrM5bfvO03ep/fs/+M1ga9Zb2vb65jW19z1f72mK/jBOPF7vQt0N4i9xfTIRdxPwPsZR9E3mloq5ZQXxLwAoyFnkboRU+F0qckBCz79Ye35saetJzJHtNQCM4pGsn0ScOTVuFQ8nYlfjlpNn/qC6IHkfAFKx79MY55kqx9yBTsQNGsPE/qlap+94DBu7mKuzqaVtjxa8FtucLGK/LV/8esXH11zx+TTbmC/ajcqVfP8ykkM3PZ/j1eU1td9cXlE7q0+HjsfXYRD7Kw7dRLZ9GHhblYdZPatqjWEQMUH4VEfmOQof3hUVr6aJnXu+Rn3P7YmKV2w9VGoQ4YCFiUwGJhU3eWvmCyoqDb1YP+ILKq7rSkC0Z/9A5Usx8Fz8g5y071Xu74Wd9ZHwtr04o2pvWPOB+eqOzMS6TWy3xLlXcxvEXo3kDj+oPkS+ZvldrQ+LgwCk00wJeebE41Kl6jGSS0QIQirmWIpYkZHz1QtHIqXH27GwOxAAlDEfbcXZZ/eRt8+MazKK069KwFH8YSQBsVULuwOTyJM016rni+M6iD+wvnsxDhUIk4zEdHUuBmUXdQZ7dyT8KBU1Mg/pvG0uYkIqfIPdyb+wZ6jcxc458XNA39FSMk+1B9sNry87Us8CwHYd3yW0NX/ftQu9qHvbOt4PVNpMxLr1vaq/oz82GR9HvxXrWcS4tl7zO02S8Hdm5UzYi/iekOW4twPk40tIMTGqGCR8hsVdgMdMmt8BQNTwKplP5JnqLtG0fM/Ye+0sF/Ffvo8Q+YW96tvh7u1fahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF/qhhjDHGGGOMMcYYY4wxxpi9wB81jDHGGGOMMcYYY4wxxhizF/ijhjHGGGOMMcYYY4wxxhhj9oIoNy/45KNfUHuRR+nyWTWjbeezOR9EydunTCl+5KrySiy9zOMzk4T3MfQdH59Qec+KKtiKMtoAoJrxOc6qMtiE5j36iY/75x99yu1//kfB9vyzj2nbbV1T+yS+e3V9E2xZXdC2eUrk7QEcLeOaXFxzl0zFBmfpSO1JEvcsFeu3C4PaLGKflXxu7z14RO2/9Rt/m9qfPv1msN3ciYFMW2pO8+in08jXeOx7ak+I/wPAQRH3Ox2inwNA2rfcnsRnqi+wMxU/wM9t18W+646f/U3LfWbT8jVhMSHJeNtZtaD2H/3wt2K/4Gfopz/9Q2r/7DMer5uG+QNfp12oG3HWpuinacJ9NwFft2EQfY8xFiTizBclX890QeJGytdHrdoo/sJCGAlTb/sQ9onMcSA2ABiFnWzBX9rjHxIxF7UHbcvPc9tEey/abjcxtwBA3UR/aDox94Tv75TxGJxlcZ75X8N/+RgGPmfmM+PA5zZ0PFcnA7cXY7RnpXCwYx5Lh3WM3+ubFW9LYjcATDT2AMPmLtgakRfGQsR6kgPKGc9Pfcf9q6n5fIoi1pDTxPcmzXgNlGT80LH2Scr7GCYVFKI9ETVXmnP/H3s+vnGMdhVvdmUUtT2L6ywOAkAqzqyKY7za5msxsfsIgIFUJqlYf3XhShO1pmTcwg/U8k09aa8Sj8p1skSIf2CxFAASYU/FeucZmdDI40LT8vqN+aqKT0nG9ww5P49ISb5UtckuiDw5kJiizkUjnIPescFrsnHi4wDJLwCQk5o1BR/HKHwgTcV9kIx7nMRcyF0VAMoq5odU1A1pKWpUUbtOiPtQDHx88gopggXbhaQT6zRw+0TOrYrrk6hZpK+zOnLk53MXpknUGSQ+stoWAAZRYyUixk4kH8p7gHgmfaci7inq/jIJn06TuN+ZqCdUTULXSjhpqmK9SBjsuKRindT6qfVmqYtsFwCgy0S+JT61FWu9bTfU3re8fTtF+5ByH96FvuPntSPvN8eBr2Yu7kmqPYsR4mqGfsv/MCfxWIWYgry/AoCq4O+ZEpJHhoGv/cjqJQAg8TsTOWdQZ1/U2ew+zWINAPQil5fkXTwAzBbx/jKIfRxqEW9IPssOeFwRVyBZW6akaM9EHr4P/qWGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qv8UcMYY4wxxhhjjDHGGGOMMXvBvYXCXzznAtNMzmM+X9K2y+UhtVczLpg7n0cxmEqIoZRCALEboxhMJ4R0VqsoWAkAJ4cH1P748ZNgOzjkYujLBZ9jWRABGyEyc72+pfYXLz6j9jevPg+2aeKCXfMFF96ZhMjURISgEqGsyuYIABlRjWq3a9q23XD7YsZ9KiOiNKkQFd8FJfLG5KwOhQ9893s/ovZ3vsPtd0MURb3ecBHW2UKI+STELhW4lJAjF2ViQpZDw/evFvvaE1HZnghdAVr8dHHA41A1i3Yl17UR+na1EFoiOm1YEYFjAKgyvmcZEab8/t/4bdr25MnXqP32P/w/UvubVx8R61d/LrZbIfTLxLzV84UwZaIE5Eg8mYTQXiVUGDMmkCYErhT6fwowpXDR9AsIG3ZCTW0U9i8imqgEkZuai4Ou73gOvb58Q2zXfHyiLJnNYx7OKh5T05zHJiW+PRIfHLQ67pemFmLZTFhYCSguhRDbkRC0naq4boWY2m3Jfaa/ifXB5oL7wLC54X1suW+0WyLQLYTCs7mIFWX0maODE9p0veK1zo0QCk6IkJ0Sgi5Jbnn7D0R9RSoIJV66WXMh84yMLy+FL0CcF+EPCRHkVSLku7JeKeHNmIRTMeBZxeethC9TsudKELyXaZKIZYuzmBZ87TJxIFkdy8Tb+Sj+sj1proTTVe5i41AP1eKxvItR2Kch7ntfcx9R+XIiOXAQouyFmqOoIdi69v1XL/xar3nc7IjI7EDuuwDQNFy8VtVSVRn9VIoWk316+wdytoTIqaozEnHfL8j5SjJxboW++dTGmFCIOJ1BxBWSdwBgYuKnwr9GEW86URc3bAmFsPUozvlAntmKerFvxP523KeYWP12w99d7MIkxMd7Iuj7RUWn1XoqYXiGCINI2FsD0a8SBYYQOWaCvuj5GUpFnT3Q4QkReTFutd4g01FNZfkt8jALQ6NYP1VjDR3LOaJmJ3UJABSi756ci1y03QlxjhN291Z+LvZbJeuJVA6TSuzCzASjK/FuR4los3oVAEYylkEomQ/dFxD5FsukXjUmYtwjOc9DJ3yX+CgAlOR9OQAcnZ0E26Lk9+O+5clys46xvhGC75W4e+vXKOTuLWL7ffAvNYwxxhhjjDHGGGOMMcYYsxf4o4YxxhhjjDHGGGOMMcYYY/YCf9QwxhhjjDHGGGOMMcYYY8xe4I8axhhjjDHGGGOMMcYYY4zZC/xRwxhjjDHGGGOMMcYYY4wxewGXlicMzTX/Q5oFUzNFpXQAwFjzvts5b95H+1RxZfUuieMAACTxu81ms6VNt+sVtR8ffZvaT8/Ogu3Rgwe07bwq+PgIq5qv37bmyvSrm1tq78Yh2MYs2gCtNt83fK2aTRxjn/Fxb2rexzBG1ftG7E2z5vbkiPtOVsT1njrhlzuRUGuex+c/ffQubfv0m9+n9qE8oPY7cozaTPgXWWMASIhvpKIten5uZ7w1QHypafjad1ved9dGe9dy/x8xUfuQivBWHQZTL/Zx0/M1qXve9dDFuRctb5xN3F5mcdxnDx7Stt/71R9S+0e/+Bm13928Cra25WdrF1qxbuMU7alYBww8JkG0T9MY65OE72vb8TiYNdHHspKfrVy41zRxfxyH+EwxPMkwxPVrWxG7yfMAICU58W3fcV27nvex3XKf2dQbar9brYNtLfqoFkfUnrLcn/IFrEXeuru7o/aG5KjFgsffXRhFnmVxOku4HymfnufcfnZyHGzLOY/e22Nub2/eBNvNy2e0bS1qtOWM2/sm1m5Zxucy9vzsj108twtRc81nFbUnmaghSTzOSN0LAMVc/D+hnOculhfbJp4VAJhULif5L6v5OPqO59vlckntOYl9iThzu/LxJ8+pfb2JdXmR8fnNSu6/BdlDAJhILMyEH6i43tzG+vvkmMewSdQZqZhPStZ6EudfbQtJuWIUf1U+4nPnLZWfivYTzzH1Np6DFy8+pW03W15ftmN8ak9qXwCY1B0y53ELxE/6TqzT3/kVbr8Hy5SPd5PGWFhkwkfF6k+iRiiSuIcJeOyVy0YcUpTwqMkZB4B84J3P0njvS8UdaBD3vqSPZ+50eULbVvMFtZclzyUjuXukwjW2oha9EXempon70CTqNsZ9dyTxRtXs9YbXdOPdFbVvtnEv65q/o9iFPOHrRo48RpE7JxHX8pz7XUoCpMoLKuBl5IyOo7gziD1JUnFXJ7F07FXkFRcYdj8QcxyUXczni+Qi9Zdp4nszkrFkIhf1vXgnRXw6IXkIAJYTjzcFeEwop1j758S2K7moTeek/lavfJRdQrZb1YlzcT+YV3Hd1LnYknofABqRz1hJN/XCR8XriIQElmwStRh5FwEAlajz2J1ODA/9wP9wQNYPAJ6enAZbKe5/C/FS4+cfx7ve3S3PC5OIy+WC+2VP3mlM45f/vYV/qWGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qu41DlBiLZjmrpg6zqufj4MsS0A9C1XUR+6RWxb8iFPQoWe2duWy9uXeUntx2dn1H52chxsJ0uuQF9kXPl9nKKS/Uqs39War1/b87mXi2Wwrbe8j64Re8CXCs2mjsaJ2ADUDbcXaRx3kYh9bHkfY9vw9qSbvm1p212oSr7f7zx6Gmy/+Vu/S9ueP/06td9N3Nc3TdyUuuPrUG+pGVMe+8gm7ncFOeMAkM14UEiYOeNzScsZtS/KeBbjSXnLKOyLoyNqnx+eBtt24L0nI/eZYeRrNSI63pDwEU4j9/XN3ZpYL2jb9w5jjASAX/3Rj6j99YuPgu3ls1/QtrswTTxwjENcC9V2UsFn5PaMJKk8V/mC7zez813Sf0mUoxJPnYRXi+Gh7aM/NiI2DiPvJEv5uR3I3nSdyBc93wP1zGo+D7bjrKBtj455vl0sD4Ot3vK4d3tzS+0vPv+E2jebGCifPH2ftt0F5UsTcxqSHwFgmng86UR+q4qYo86PeNxoeAmEd5+eB9v69iFt+/iY9/30JNYjAPD69etge3PJ9+/N60s+wCHOfSlqsUORF9Sx7dt4BvKKx5Wi4s9MRG3ZkYdOohYbR5HpSC6aetFWxNoi5/Xp4jCeuUzUsrvy4g3PcTc3N8Gm/jdWIu4Bo4hL00DyuHCETBzeA3InOT7gvp6o+lY8MyExIFeTF32wnCbzn1inQdRHLNkloxhgLqOfeGbcm2fPntG2n33G7S2psdqOn4GW5D8AGCDubkm0FxWPfcA/EPZ/NU9PeJ8d4vqUwjkSkfMhcsnUx5jXi7g0iaxWluQMiLphs+bxMRWHrpzFNVFnqF7xXDInd7dzcddfnJxQe1bwcbNaahD3+iblNcyC1HoA6JnbiriSz7jvdmTPyi3fm2frGH8B4PbZL6n9zevnwdZ2vEbdBZWGCuLTNM4DEMuGTPwhJfmFvcMBdIxN0mjPxC22ULFexFL2zFT0nYizz3pWcxzFurJ73tsBkueJ2JSmohglcRcAMlIH5eKentQraq9fxPtBdxXrUwBYFDzHnx7E9wsAsCBrMpvEC5odKEUNmpK7cN+JnCff4fJnsrIhFwe0EjGzJPfBbcdjoBp3Kp7J3g0g5ffPIVWxIj4zTXhbVeosC35vqIo47mng69SLvTk6iHdsADg9PIi283ifA4CTQ+7T7B3Dzz7jvjuqdwZbvmc1qcfG4cv/3sK/1DDGGGOMMcYYY4wxxhhjzF7gjxrGGGOMMcYYY4wxxhhjjNkL/FHDGGOMMcYYY4wxxhhjjDF7gT9qGGOMMcYYY4wxxhhjjDFmL7i3UHhBhEwAoKmjKMioBMFHLhY1dUKIjYhLNULQUEnQ5VkUzcmEOPHpAy7wc0oEwQFgTgQC1TiUyGNP7ErQejHn36De+fp71P7mNREI5FqMGGouPsOE4AFQdWYlfMyESgHgcBkFbI4OuYjnTcH3LKmVIHEcS7/66oXCH5w/oPbvfP/Xg+3k/e/QtquRH8N1L0S4yJ50LW/bC7FlppE0J+LcAJCX3O9yoWJWslghhMbShIs1zWfRnjOxJwCDEAdNq+hfANCk0ZdqsU71xONNL8Sk0iKOuyx5H0PLhRebdbTfrrmw2WbFRMWBD77+LWr/W3/nHwXb7/3zr/7b9ihE50cm3CaEv5U9EX0zEbqMiPIBwkcBlGXcv0KIe+VClDMVIoMsB3RC9KsTimwtEwUXYni5EuATWYrlokSI8uVC+LjvuSjn1fV1sA0ivlVzfm5n87iXg/CR1R0fx0aco3IWRdZmcyX6+uVJMuFLROCuFCKprZjDs88/ovbVTZxb8f3v0bZVxs9LTkQG33n8iLflZvTvv0vtbROFUl++ekPb/tEf/pjaSxJ3f/Sj79O27224z3z8/BW1P//k02BrxfnMhUijEnFOyBFV4rNSCJoUY5MQ6pxEHTqxgQDIyHwypZi9I/3Ea4Epi/m6JfUdAEwiFirxQlbHKjHvSgjTzpZRTP3giN8ZMiL8DQCTEG3FRPZFio0L0U+y5+pxTJgcABJRvzExb6Kn+dae3Pu6CQCoqngODhY8JlMhUAApeWZJ7oQAkCqRdHFNrtsYR4buqz8bZwd8zneXUcA2J/dxAKjmXES0VPdpIqLdtFzQut9ywdCU1DZJyp83U8LMQvj14DCeORHasNryfFkMcT4noq6fiztsIsY3kjqtW13TtqV4X1KJO8kBSQT1xPemA4+pNREb7zcvads3V1H4GwDym0tqH6+uo2346u/ehahVmFmd7V4c11QEMRplRDAdhX0iuUuU6sgzJeat4kz0x1TERpHy6T1FzQXyLiaExcldXeVbsWVI1VhIzVNN3O+mqxtq7z78RbDdvviYti0r/k6qOeUizAmppQpRr+xCIt7DMU1roXONVNWD4s6bE78rcr4+lRCATyYiZN6Ld8bqXeOS50pWNwxEoPqtXdRRSYzThfDdhah1DlMhFE7uhb3w/xp805o1zwGrq3gXPjk+oW1PDvk78HcePw22qw1/f7XpeT3Q6wsMMX35Osq/1DDGGGOMMcYYY4wxxhhjzF7gjxrGGGOMMcYYY4wxxhhjjNkL/FHDGGOMMcYYY4wxxhhjjDF7gT9qGGOMMcYYY4wxxhhjjDFmL/BHDWOMMcYYY4wxxhhjjDHG7AVcip1weHjA/zBFpfh2y9XPx4mrzY+9UKFPoyx6goy2zcg4AGAco1L86ekRbfv08QNqL1Le9+ub62C7XVe07bblc7xbr2PbuqZth9UdtS9zviY3+SzYkqmgbdstH1+zaah9u4p7nOZ87uXskNqXs3nsI+HjU/Y8i3MEuE91DV/XXTg8fUjty/OnwXbX8j6u2w21j2LOzRD9sSN+DgBtz+1ZEtenmvG1zGd8HFnJv4kmxB2njvfRD/GMA0CflrGPRLXlfXcDPxfrOvazafkZ37YJtU9kfAAwI2dgFJ+Oez4d9GP8Q8e3EduWO1XRc/v50yfB9t7Xv8M734Fx6NQf7t12GlUfPFYlU9zvMRf7pzYFce2HgT9vW/PxbdYrar+5fBNstzc8pq8aHhPSIvr644ePaduHZ2fUXlXcd6s5yxd8/eqa54WrizhHAPjok0+C7fLNBW376MkltZ+SWNsK/7+5vqH2+eEJtX/9G98ItrNTXg/swpQIv0uj75Y5b3sr8tif/skfUfvVy7j2v/j1X6Ntv//tuA4AMJF4kom5ZGLcKZkjABwsFsF2fnZK254f8zp0Rvr4/q/yObbg+eKTFy+p/T96+SLYNmt+boee780kchSLN5mo50Di29seiF3kyqzgZz8jcQUAkiye/yTlMWFXqjLuIQDUffSndOI+Noh6Jyl4DMcU2+cZX+fDij/z4CT6ZDHjtfAglm6c+H4NY6xLRlL/AcAg7lFkioA4u6k4u5kIWyI9UAYx7kT0UVbRJx884DntzfUttTdDvOJOKd8biH0fRK1Qt3Fh+57PcRcWc35mmyHewW4//4i2HcS5X7zzLrXPjuI6p228qwLAiz//Y2q/uYp70ohabyty2vL8EbV/93f+QbDNjnnOQMvfR2xffxZsnbjrlyN30uHumtpXf/Gnwbb59Je07VTwvnvhj5sutq/Fvb47O6H2Jo/nYvPqFW2b3fB69kHF+54/eSfY7jpeL+5CKnxppEFW5EP1/3pFPGY9JyLXJqIP1neigiB5BwYAhaojST+pyNcqUpHrp4zRrG5924nonYw7SflryFzNUVyc2T1y7PjZX795Tu3PPv1ZsF2+inECAC5SPr7LC/5+cX5Aatf5krbdha7ja98T+yDylarxyoLv1bKMd8cqj+/3ACBJeB8teUeq7plDJ85FJt5BFrG23Ih8dnfLfaYhNf+i4P5fltyeixqou4v3/dWGvwO4uuHjW215Dl1dxnmqe+jXvvltaj84OA6246NoA4B84LXGJN7FVE3c90a8j7sP/qWGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qv8UcMYY4wxxhhjjDHGGGOMMXuBP2oYY4wxxhhjjDHGGGOMMWYv4DL0hJPjU2qfl1Ftvt1y1fambal96KP6OQBkWVRLz3OuKo+B9zH0UUV9uZjTtg/O+BzHvqP2P/rTPw62P/3pL2jb18/f8L5XUZn+7NEj2rbDRO2fvnpN7TeXl8G2Xd/Sts1mRe1Dy9e1XsX2xVwo1idc9X7KovtNSUHb5uUBtVdlycdH5tPWDR/fDiwWh9Sez6KPdRNfn3Hk9kmsW5bEb5GJOBaZPOLMlwbaMs35Gic5/yY6jLGfWxET6vWW2huyfpn4BNun/FzwaAPcEZ8eUr6AScVjRZrywXRTnHu/qWnb+uaK2tk8D46WfHwTP5+bFT/nbRPHcvzgMW27C6nwpaaPZ7AjYwKAruW+oeJxmsbzMmtifgKA5YKvZ5rH+MP8GQDevH5J7b/3L/4Ztf/0T/8o2C6u72jbrYhVs/ki2P7mb/0Wbfv3fvfvUvt7771P7cfHx/F5BY/H7TKOAwDa/j1q/4Mf/yTYXr/5KW17ccV99/j4QbCdn5/Rtg8fcZ9+972vUfuDh7HvrOBxbydETO+H6GNNwvOC8se24+fl8k3005/8Me8jGfhZPDs9irajaAMATNxnpoHH6RmJsQcHPN8/fPCQ2hfzWbCdnvJ6bgCP9f/gN39E7X/0e/8y2D782Ye0bU/iGwCI9IKsjGtVFjxnTwXPOX0X7RPJQwCQV3GdACCfcV8fiF8OPd/HXckKvufLZYzVA/g5ajqeDzthxxBzidqrLuN93DXRfrPiZ5FHRxkWaG2o5jKqOZLOc1G7qXEk4g/c/sX8YxL1L7n+oay4n2apiDkjqZVJjgcAcdVB2/O5j4j9ZOp+ugMHRyf8+UfnwXb1h39A2/bi7o2cxwO0pI795ce0afPnP6P2m9fxznstarfnd/EeDABHT9+l9u/+nX8j2EpV02V8T5qb62Brq89p2+TFC2p/80//KbVfkTXpN/wONBdncRrFvaaPe7MRZyg9FOdlFvd9KfJzI3zkwd/4e9R+8ms/DLYrEmd3JSG5CQDYNTsR+QLCrlvHNUrFxTQj9xEASIg9SfjaT5OKu3x87A40TrzvQfgXSO3A+n07ED73cVL/X5rUKuLlhRo3RAzpyDuG9oa/G3vx2S+p/dVlrJVvN/yOdiPy3E3Ha+iDJtYER+dffS01DmKv2J6IuAFhFq89MMvjPTvPxLu5WtRRNzEHXN3e0LaJ+P/4BwfxDgsAE3mftCHvXgHg+nV8bwoA3Ta2X5zxmvVgyWNmJc5RTd5fj6SuBIB2y/1/veL2N0l8F/ryOX/3dPaI+26Rx/v+yRG/Xx1m/F5YHfB3Mesm3pne3PBceR/8Sw1jjDHGGGOMMcYYY4wxxuwF/qhhjDHGGGOMMcYYY4wxxpi9wB81jDHGGGOMMcYYY4wxxhizF/ijhjHGGGOMMcYYY4wxxhhj9gJ/1DDGGGOMMcYYY4wxxhhjzF6Q37fhfBbVzwGgKqLC/bjkivDDMFL7OHDVdiYUn+V8yM2GK9nfXN0GWyXm8uCUq7nPSq7afreOqvI3D49p23oT2wJAV0zBNpVc9X51w/u4vXhB7eubu2BLM7KoAMqMr2uXxfEBAKZoT8H3dxpaam/qqHo/TRltm+Yz3nfG2/djHF9d83HswuLo5N5tm4b7+dgX/B/k/JvjlER7mvB1SJKB90H2b+j5+IZWhAkx7HGMz5yI7e04RB/sDxP3XdoWQD/wczQQe1bxmFXMhN+N3Nc35Mxtby54Hx2PWV9792GwffuDp7Rt326o/eVr/szVpg62rOBz3IU04esDxLWfJnEuRF5omjgHABj66GNtE2MMAOQi3o1D7GNMuN9tVzweX716Re2b9TbYSpHP6oTP8eL1y2D7w3/5L2jbkawHAPz6r/9Nav+bvxHt5THPZ2XJD//D83Nq/7Vf/UGwPfv0U9r26pL77uFRPKPf/8H3adsnT55QezWbU3tC/GES8WYXJlEDdV20jyKfbjY8jx0eP6D2p1/7RrAdHR/RtmPG9/Wujud2APf/Qq2bCAkpWfuTMx6Tjk64Py5nsQ7Nc54Tc/Dxfe9b71P7P/6Hvxts/6Tlsen1i9fUnmRxfAAwW8ZaNC/4+FLxf5DaNsa4thO1TsrXBCnvuyHzTEht9VWQiTqb5ZJUzGMQ9U4ziJwxkDpIpS6Rp4ZNzPmbM36+8pLH+zTj68+sichH9MIEICc5Rj1PHA1ZY7E6MhN1a16IGlX49TTFGHAu8svhkp+7dh33LM15jNuQ+wgArDZ830HiVinO7i7MlktqH05ivJ9InAaAl3/2F9SePHvOn1nENbp7yeua1cUbPj5Se+Vi7esbXgtP1RW1jyQGZWTMAGTMa7exHttc8Dn217zOvv7wl9Q+XMeYMAePTQetOLekFgWAgcTkpuf7nm5431kRfbfq+PPGjOfid77D9/1rT+IZ3S54/tuFVLybYPld5axe3ONUCmCxdyI5BABSEY8TYk9U4BUjycR7nIzEdTnHTuQzdm9W9yVqBSBy1EjWapy47/bCH/s7XnfW19Ef28tntO3Lax737roYEzqxNb18NcbHPS/jGagOTngnuzCJWoLMYxT39L7le7JteJwuhth5msa1BIC7FbdfX8d93dS8bmPvnQEgEeMrp5hDlymf+0KUyD05c2cVb3wu4l2WcKeZIbYv0kPetuJ32KM1r186cububnk+eybqAZD1vrnm+zjlfI5Jxuv7aYp5O0/v/Wki4F9qGGOMMcYYY4wxxhhjjDFmL/BHDWOMMcYYY4wxxhhjjDHG7AX+qGGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX3FuNY0xEU6I+kwuxyVKIOhdCUKkgQo/zBRdN64To0eI4CoV/81vfpm2fCBG6WcWFsh6eR6G2v/VrP6Rt606IFhMx11oINn8ihNr+nz/+U2p/9lEUYiUa0wCA25sbav/opz+n9u3NdbCpfaxved+Xl7GPesMFbNT+ti0XpemI2mMrxI924eCYi8uDCOJ1QkAqJwJSAAAh5jgS5SolQJQWSjQq9jETOnuFEP6ZVzwmMJGfseeiR91ciDUtooDqJL7BdiO3z4WAW97EZw5CVDYRc6/XXMCq20aBwL6ONgA4OuD7/o0P3gu2d97hsen1ayFs+EoJLMZ9r+ZcfHcX7m6EYC4RpFW+W+U8nkxCcLQdSYwY+ZlnAqcAFwLMRN5aEh8FgHfe5aLuR8exfSHELa+vr6n9s89fBNt6zcXRfvwHv0/tP/8pFw29uo4+87u/8zu07eEhF8JtWy5QfHwQxc3ef/9d2vbBA+7rP/zRbwTbN7/xddpWCpI2Snw+2gepGPzl6YWI9kTETBtRM2yFoG01j0LqAPD067HemS94PE6V0HId8/J2zQUbJ1G/TK0QIiUigzMh6J6KHIqDeLZGJQovhGMXQoDv3/ydvx1sR6IO/Zc//jNqv7nlZ3RbE9HXmrftRT7LmK8LgcV+4j7dCqFZVp8qnepd6cW9YaSKnHwQnThf/chFCkdSq1RC/HEmxgeSv6ol96U5EbQHgJII9wIASJ4ahOjtJPZ2JGem2fA43TQiXxJxYgCYiFjv0QmPLWXJzwwTtwWAGTmnZ4/OaNuD4xNqv9pexHEUPG+re2ua319MeNpB4FKRiXFlJL5dXMb5AsBP/pTfER+K87IktXYmhKuVkG42j+OeHfO5iFIPt1dcjLRbx38wpvxsNUJw+DURBV9OJ7TtsOLnZRJrkufRD4SuNZKO+1fRc/usix0tRKmSkPgNAFMT+2jEHTudeL0xfszfDWSrWEcezXittwviagumfazEm1WFN4HngIHcn0YRd9kdGwAKcrZ64Ufqfx1nYvLsPjEJ/wf4vrZEKDwRc0zEK8REjJztwzTwttuGn7m7K3HnfRNFweuLeF8CgJV4z8ReJUzi3UBK7rIAsDjgd6OH738j2J68/03adhdGVR8Qe8+XGNuVyPeitu+3cV9nlRCGFveqZRl993TO66iSxFcAeFzx/PKE5KKzlN+XFu/F97oA0G1jLnp4zN8Nnx7zcU/kbAFAU8RYsVjwOR4e8bhyJO5Xr66jr69EPffTjz7j4yO+s9ryO8YkBNjn4p1Uxl48quB+D/xLDWOMMcYYY4wxxhhjjDHG7AX+qGGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXsDl1QkDUT8HgGSK30WKoqRty6qi9iLlau5ZFm2zOVesPzriqvfHx1HJ/tE5V7cvC96HsucpGWDC5wK+fJjIH/qBq8cfL/jcD+cLav/kydNgq+stbfvjP/sptf/sT/6M2tu6CTYxbFy94ar3r49eBtubl89p283qmtrn5Rm1D4PYh6+Y49NH1H5wch5s/ciP25gQPwKQVzNuL+JCdx1f/DLn3y1zcubyhPcxI88DgOWcj3s5i+clT3kfXc8Pxoz5+sT3dCQxCAC6kT+z2nTBdrOJ/gwAm80ttde33N430X6w5PHw3Xe57zw4Owy2duhp27tNTe1dx9sXs3mwVSX3s134g9//p9TOYubRYZwvABwuuF19ix/HIdpyvvbr1R21V2x9RN66W234OIY4DoCfuVLkvpLlFgCnh/FcjF1L267XPNa/+OxTav+P/q//p2B7/fmHtO0H3/gOtVciZo1kmu+9+y5te3b2kNrfe//9YJvP434BQNeLeNPxvUlIHp5GfoZ2YRAxDIj2RsxhLc78JOLg8jDWO7P5krZt1mtq39TRx8aW+90g1nhQ7XEZbOuGtz0o+RzPlvGMnp+c0LbLA15HFWJN0jTm7R9+//u07aOH3Hc//OwZt3/0ebB9/tkntO16y3NUxgrlhK9T33Kf7lre90B8MGXP+wroRz7mltw9EhE3k5yPbXbA41JCap5lxcdRgvc9TrGeSESOUvcoVfN3Tdyvize89nj96hW11+uY625vee6q19wP+obbZ4t47r77w+/Stu9+EO8jADCb8RydkLtUnvMauhA1TN/F9c5nvI+84ve8QsThYYp9k1CxM0nKfSkr4sOaDY/f2w2vBUYRJ1JWl4u6phJ7kiexj9khjz8PSFsAqOt4tgBgWJM9Gfj57BseK/oVWZMlv0tPN7xe7EWNlW1jju5FDd8PfO6tyIFFE/chm7iPytcRZN8zUj8DQE78HACuP433dwC4+sOfBFv1XX7GdyEX4x3YOymRF4aJ2xuxJzWxpyIXqf8xPJJYP4m7ai72T60mSy8sTgFAmvERspw4iPGJLpCIPMfupV3HJ6nuVxevX1B7/zrWWOMq1paAru1n5F1fMom2Iuc8eMBrwLMnT4KtOjqibXdC+EySxDhdiJyXpeIdbsY7PzuIcfN0Ke7v4ty2mxjvKnEASuF4ywXfk1NSp0wLPvc5+P51m+iPc94FykLU3+I9HXUxdVUUc2/EmctIe1ZXAkC/5fYtyf3bnu/jlIqzT3IiABSIa5JDLOw98C81jDHGGGOMMcYYY4wxxhizF/ijhjHGGGOMMcYYY4wxxhhj9gJ/1DDGGGOMMcYYY4wxxhhjzF7gjxrGGGOMMcYYY4wxxhhjjNkL/FHDGGOMMcYYY4wxxhhjjDF7QX7fhqttVKYHuLL6lAjldy6Kjmngiut1sw225d2atj1YLqm9yMtgW21ivwDQ9nwcVcVl6JOE2UVb9floiu3biavKX95cU/urNxfUvq2bYBvHqDT/1s6f2TVcsX4i7bNUTFI8c2hj31N//+cBwNhze0q2IVfj24GHj9+h9sXxSbDd3m1o27rpqH0+y6g9QbR3NT+fVc79MU/jYRzEOSzYYgKYFXw9F1Uc33rF9+luy9ekH2J7MQwg4WFsFGexJ89sbm5o23bL/THt+J49OCqC7dH5CW37+NEhtY9jPLfPX9zSts+fv6D2puXjOzo9CrZytqBtd+FP/uU/p/a6jut5dnJM277zlJ+t2ZyvW7WIOeDw8IS2FSEJSRJ9t6oq2vbqksfd1xeX1F5v74JtXsT8BAD1hp+LO+KnXSfOrcju84r/oVlFH/u9/+qf0rY//bM/o/b3Pvgatf/6b/xmsH39gw9o26OTc2qfzeI+jJPILQP3/wS8fZZE+wAes3ZhS2sGICO10YqcFQBYr1fU3oo8cvIgnu9qyc/ctuZF2rqNvjE1fC3TiectpNzX2yGuyWrL53J3w9fkxeexLjyYz2jb45MTaj845muSkzNaqMMlSoyzkxh3AWAiR6CIKQQAcHHNa9+rm2i/veE+Ut/FGAQAHcm3AMC8IRXj25Ve+E1PzuzQibMpCu1UuOQ4xBluRc5vBr52/V2Myc9e8pj8/OUDau867h+vX18H2+/93p/Qtp989BG1H5Jjt5jPadvZnN+j8pLnwPksnrFWXPTW61jXvEXERHa3FPF+echrmIyMe+ShBWnCHbvI+PimKc5zSsQldwemkftSs4p1xsXlS9q2EnHpyYLv68mG3Q/4/qUq6JGlyMQd+5ScQwBoSD0GAIss2hNxb+4uX1H7+tmbYGtFDGo/fU3tr1/xWq9q4zxPhB+x3A8AfcPvdHkdHZjvIpASHwUAkHUdxDYe5HxN6g2Pk2/+4I+CbdmIpPHf+29w+z2o7/idqB7jeIvFAW2bijyeiJhU5LF9VvI+SPh6+0zyHiIRcS0VZz8teefzWbQvD3isH3o+7nUd7V3Pg+Y48HGvV/wdWzLFfmYlrwuzA+530xFvjynm0PN3z2jTsuF3y/o6xs/NHT/jmTgX+RF/ZjGL6zp0Kid+efKK171VFqPEXOT1IuW+cSDuwg+O49ofVOJuu+K16etn8V3GNPAYU5V8jguxJxmpb0eRLwpxR0sLEldyfg4TWjnruJKQfCbShXyvlYt/MJIE04ukMxYi7qVxvaeRx/QOPGaprw0kXGNUeese+JcaxhhjjDHGGGOMMcYYY4zZC/xRwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/wRw1jjDHGGGOMMcYYY4wxxuwF9xYK3whxbSYTtM14WyXyVgux4O2GCCOKzzCzXIi8EcGb1eqatp0LYZtSiNIwYexMiMwoMTympsZEkgHgk+fPqf3jTz/jXRMBzkIIRydCCKoiwlgAMCMCgcsDLtaXZ3zfm00UXkxHLo6WTlx8Zup5eybA1Le87S4shBgpiOj2+uaKNm0aLhZ1dMhFGw+XUfhrI3QzM/A5l0T0KBMiQcdHfI7vPeLilot5PEdMFB4ALl5xYcPVVRT3U8JccyF6OQmRwZQI8D064n5+9O5Dap/NeKyYL+MZOBb7WAqRuqaL5+Xmip/PNOHnYr7kgl7HZ3HPquqrFwqvax7TN+sY03MhLFUQ8WYAyEu+nksiCi5SDhIhCprnZF+VoJhQ9M1zfl7Wd9HXtxMXqFexqiHi0Z0QhZ9IDAKAbFQiuzEXMWFyABhGLuT13R98j9rfffpusB0eccF3XlUAbRPn3guhwqHnc+yFUOnE9lil7B3YdkJwsYnjuhNi8UrIeBJrwUToKpK/ASAteCzddnG/6w33UZFGIDT1kBHRyywXMV3oxzHfuFnz9as77gNXd1zAMCdClqz+AYBK1JCHSy5U+uTRo2D7zjfeo22fveb1w+/94Z8H2ye/+JC27bZ8jp0Q6mUK27mIqbuSiHg/kNq0ETEvITU5AGTifpAQh2qILwHAJNauILVNmfL9luKPwrFL4k9f/yZRlwfw9En0JQB4chbHMhMC0VmmRG9F7bWI4ytLlRd53ypWT2P0ByVMOxdivSVR612veFzo1OYk/EyzHeuVCvkODC2/T1/8/MfB9vyzz2nbUyFceirug8uU3EvF3ZYJpgNAQvZvWvG7TrXl9qcLcccg7pjWvI83P/4Tav/ZTz8JtsM3/Iynd9xnXq55rDgjNcxhwn2UlPsAgFb4ekX2kp9OoBB7M5HxVWJ8D8Udd1Pxc17fRGHlq49ijtqVF5/xHHe9iWewz0RdQwRwAWA98BVNinhXyha8b/Z+CABKIrZckPs4AMzExi4WvO9Vyf4B96OLywtqv7yM+9eI+3svzu2a3PMAYLaId7dvf/NbtO2jQ34vzQ6431V5zHM/+NpT0Te/Qw7rWGPdvYnvIgDgVghev7njc3++ju+7VjfRtivqfWVO3oVm5G4A6HiiLtQdqZlWDY+Zly+jIDgAPP8s2ufinc/snOeFXpy5LbvzinTP6k0ANOGre/AEXtOI5tTei8YDuacDkO/Gqyr+oRh441q86+hJLlfvqVvxDncid0gAyKlSOK+57oN/qWGMMcYYY4wxxhhjjDHGmL3AHzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qvy+zbsudA5piSqojdTtAFAOgmleKKsDgB92wfbdruhbddCiT0hCvdDx/t4cHhA7Y9Ozqh9nhfRmO/+nWgSqvfNekvtRcq3sSjiWMYhrikAVCVXmz86OaL2ceiCbbHk69es7qj9dR3ns7m5pm37ke/v5paa0bZxj+ttzRvvQJELn57ieLtmJfrg+/f0Cfe7R+cnwbZZL2nbseU+MyuyYKtK4s8AjsW5ePz4EbXPZ7NgOz0+pm3PT3jfXR/9dEb6BYA85WduS/wLADab6AcHB3z9ToT/pxl/ZtfHWDZwF8E4keAEoK6j7zw4bWnbZOLjGFN+npdkH5qGj28Xlgf8+c02Htix4+fy+vKC2vvxitqLKtpXa973g0cxfgHAWRv372sH3EcPzx9S+7e+R800Jrx58RltOyQ8Tk/E1+uG+8ZmxYNjs+XnYiD5uRP54r2jE2r/4Fvfpvaj09NgGyce90ZxYLo+Omrb833sO7F+IrdOpGZpW76uu9D0IhgM8fmJKM8mxNgNANuWr8XtOtoPT3jsKeZzap+fRF9PKh6PS1ITAkAOPvfFPM6zOuDjSLqK2jHG/R57ETMLnuemjMesJI/2KeV9DCruHvA88uj0JPY98X3MxDP/OP3TaBx5HwkriAGk4Hs2kbormfjZ2pUp5TXeBLK3Ii5NIqcmKfe9LIvt84y3HRI+vvky+uTDJ49p21NRTyxJHwCwXET74eGCj4/UTABQsfoy4XPsOz7HQVwAexKrR1JfATr2tjU/pxPx1Sznsa+teRHD4novfGdIeN9jyn1qIPdWdi/alWHN70/N62fBdiTGeiz86+j8kNpnVVyjzQX3gbbh65mRe2lRCT8XfcxnvL4t87j2OakPAOD2lx9S+x+/iPVlc8XX+mEhahXxruPBPOaB8kDkS3EuhjU1Aw1rz/c9E/EepI7M5/wOVD08p/blg1jTAcBlGX3qtlWT+fJsr15T+9VtvPvfbHmMudry/Xt9y32p6UkOrvj9AKKeKBex/fyQ34+rGc/5mPg7LNQ3wbS+ekOb3l68oPZmfR0fN4haKuUxEyKWnjyIebHIeUz42kPuX3/2Jz+m9uHVp9H2S56Hv/GY+3RGasbVKq4pAPQjj013IldeXF4G25q7304Uor7tSX1wJ+7H2SjixlzV/HHOTcd95vqGr2dD3gOcHPJ6qai4f/UiB/SkHh6F7w4iHrPcLkpWpKK+Uvaa1BJbcf9sRUxPKh5vTh/HNaxEmXKx4c+8Iy+J8oGvXwXuf3kl7EVck3SH9+j+pYYxxhhjjDHGGGOMMcYYY/YCf9QwxhhjjDHGGGOMMcYYY8xe4I8axhhjjDHGGGOMMcYYY4zZC/xRwxhjjDHGGGOMMcYYY4wxe4E/ahhjjDHGGGOMMcYYY4wxZi/I791yiursADAO0Z6k/FtJknHV9rzgqu15HtXVk0R8hxHmjNibLVd4/+iTT6j9+9/+LrWfHh4H2yjWCVz0HiNRvb+6vaVtLy4vqH1ouZR9VlWxbR9V7AGgLPjeHJ/GOQJAX2+iTfR9d31F7TnZnGYT+wWASazrbbOm9vXmLtjqDW+7C5sbvlez+SLYCuGjeVFQ+9FB7AMAHpweBVszj2cFANotP1vsPOcZ76MfufPerra87yT2U5Z8jt/8xteovSDti5yHK+65wGoVfQAALt7Ec9T3/Ax1NZ9j0/H216vov+stb4uUr0mCaN8MPW07Wx5Se5bzfe+HuJcrMuZdOT7mvntzHXeraXnc2DY8Tm/rgT80WcXnrfiZf/man9vzJ9fBdvL4Hdr24VMeGxcHB9Se5nFfF4d8/9Y3cRwA0GzjHJE9p23bhvvuzQ0/F+ttHWwqn50cn1D7t7/5K9S+XMQ1udvw/R1G7ut9H/d9teb724hzS9LtW/sQx7JZi/i2A1nCo9VIYkGa8TM8pNy+EnHmxcs3wVbMl7TtYsH98cm77wYbKc8AAEkv9lXsSZ7GmDQXlemw5evXlTHeTGL9ytmM2uckZwNAUcV+xDbKOnktas6bNMahoSVnHMDnn3xG7a+fPwu2bOJnaLngc+/VuSCFdV7c/9rwRchSvqgZuwekfH7DwHPDxC4CAECeWah7CqmnAWBWRv8gaRYA8OaG51p1dpMkdtQ0fO6tuAeQ44W+E300PBc3W24fyVmfui92/tciH6WzuK4PHj2ibTcd39+xj/OcxGVsTLjvJOIsgdVkwv92Qa1nSSrf777D1+d45OM6OuZ5YHoR41L9POYRAFituW8cF/HcHpBYCgAnSz6+Rl2aSM041nwcfSvqSLImt8RfAOBhyWPe2ZzH09MHMY8uT3hdOK35+HrybgXgsWUQCSkT8T4lOXAouS8MotYbDrm9T+fB1oj8sgvHIh7Xs7ivObmTAkBacv+6rXmMuCF7NZHaEQAmUR+1Y4zTm57nfFUv9i1/p4I62vvtNR8HqfcBYGjj+iUTX480Vzmbn/N2iO3fXPLxNTWv7X/58UfUXr/8ONrevKBtP/kw+igA5KR+y0Tt8I64F2biXCRZrPE74Tu7cHDIz3HXRL/bTnyN057vdyHiYFVFe8IKDwAHR3ztizyu/dEBb7tteR310csbau9ILD0g728BoJrzuDKR+ahrAHu3CQClWL+WvGO7E/VcJ2LC4ZKv1cmDk2A7EnfIhajzVuwdTcrnkot3fZmwp2mMN9345c+Ff6lhjDHGGGOMMcYYY4wxxpi9wB81jDHGGGOMMcYYY4wxxhizF/ijhjHGGGOMMcYYY4wxxhhj9gJ/1DDGGGOMMcYYY4wxxhhjzF7gjxrGGGOMMcYYY4wxxhhjjNkLuHw5YRJK7OyzSAKues+twDT21D60UQG9EHLzs+WM2peLaE/FSLqGK67f3lxTe/PwcbCV4KryI1G3B4BtvQ22T599QtuuNze874l/mxrruGdjz+eYic9bZ8cH1L66qmLfm5G2LQu+JrN53JsiL/jz7m6p/e72mtpvbq6Cre8G2nYXPvz5z6j99PQs2DabDW27fvWa2n+/iGsMAM8ePQy2rl7TtkPLn5kkccPHhB+uRpyLLM2o/fz0ONjeeXRO2777TpwLABwdLoOtyHm4ylLuvA05WwCwXq+C7fb2jrbd1HzuN3d8XS9v4j6MKffpg+NTap/N4tyznPtCNePnc07OFgCs7uL4rl58TtvuwnwhznwV97DuefzvRc7pe36OpyTGn6TmPrraPKP261X0je/88Ee07a/8yreofSbO7emD6OvK78qC+0yZxz7OnzylbS+fvkPtn332KbV//FlckzsRd5+8y/s+JnEPAKpZ9Md1w/e92/IzVzfxPF9d8th5dXlB7ettzZ/ZxbE0W37GdyHJ+LlAH323nbgP9Cn3r06Uc9c30ceyz7n/n57HmgYADo9irJrP5rRtKcY9Fnx80xDXvu957K5JTQgALYkJScbHMasW1F7MuT3P4rhVLVsLn3nxgvvpm6mJ4xi5j15dXFJ7mcXRnJ0f0bZTKvYAPE6OiDVBQtbjq2AiNQkAZBkZ28RrlXHgNWhHzhcApKTmEW6DMuU5tSTDe3UR8wgA3N7yvS0TvqaLigyG5DkA6ER9y9YkGXkf6n4wdtw+DTFHl6k4HaLvpotnAACqPO7NKO6K48D9YSD3rroT40t5H+J6BZC+mT/tyiTuDRXJJU+e8lpgTvYJALoNvze8eR3vTz++4vfP25bvyQ+KeDCOxPoUFT9bXcVzXb26DrZsxWNe/g6v054c/zzYHpJYCgDviHvw6YwHi9NHJ8E2X4q7bcvj+p14Z7AiMflQ5NaR3KMAYHEU12or7hjXCz7u9oDn/5sirlXafPXn4nHJ+1yeknnMuG8MS34Hq34Z/R8A7n7yIthq8HXoSm5vyPuQaeJ7nYz83E6DaE/uHmnG514ueb2DPt7fp4Q/Ly/5uc2FL6Ukub645bXemxt+91j1fCwj8buLRPjuRrwL6mKsPV/wdfrg6AG1P/rmN6h9fRDH14v7yy48Iu+HACAZ4py3ax7/R1FLzHOeDFnO7yae78sZvwPNyctd9a72p5+8pPY//tlzat+0cT7vviPeSb37iNqrw3ieB/HitBA1w6FYP/Z+Z1BltqpHyPgAICO5NRU1fMWvAciGeI5S8V53vuTjKHLROaln+/HLv6v1LzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qvur/gnRN4SIiCUZlzAKRVCv33LheKY+PEoBO7mh1yY6NFpFElaLriQyazkwiddx8X9rolI9XzOhbkGMe7Lmyi+9uzVK9q2FeuUCSHijgjwjj0XnsrF3pwI4debmyiEmCZc9PZICCIfMrEyItQLAOsVFzRa3fFn1isirpcIoZodePnsM2oviBDPgghFAcDV9TW1/8VP/oTaP/yL2HcCLqxTELE+ACiJrysBUCai+/aZnDcnJ8HWCNHnVAgib5o4n0KI4VVKJE+JI5KjKHRE0QlRsq7ns68WJ8E2P4w2AJgveazoiQicOLZY5nz9joRo1Ir42qvPP+Sd7wAT/wUAEAGoSYhCDZOwi/Y9eaYIu+jEeq62UbTuxXN+xlerH1C7FGknomwXV9e0bbvlce0xicdPnnBhuG9+/TvU/q+NPJf/xYe/CLb//P/xn9C2z59xoekXL7lQW0HizSjFwPimtSQPv3zBx/HhL35G7estz6HszKVCuHgX1Jw7IuY7CvHIXIiqzpZcELLbxJy6FnlzGPict9s47rbjzzuZC4FuIaqXEPHqbcPbXt3x/WuIfSFqwjl4nTcmvP1IxpcJUeEk575bt1z0MiU5Sq3fo3NeR33vOx/EPk55rbGqeeCrRX7uiACzyom7osRSB5KcJyGi26paRbTHGP1M+WkmamSQO9CW1C8AcH3F61vhNjg9iWesqrj/TlL8O+6tyou5mjsRpQaAaSJxSwiZq/pSiUSXZTwHnfDTrRB+rUm871s+F3VvTYTqZ0rE3cdE1D07MK65kDSaWKssxR0DRFgbAC6eX1D77794E2x/IHInj5rAIxLbnog7bCLEeLOH/P7ZEsHVYsZr3q//3X9I7f+Q5Iz04iPa9rzgPr0QvlEQge4u5T56Re7pAPC5uJTc5NFPSyHkfC3e2zzKY/vhAb+PXM34Dg8nPE9N5yfBdrjife/CsuFi3iXielaHUfwaACpyVwWA54c8Fvwx4nuFVoh2Q4hUDyQXidSHRNTC6vKdUoFu8T4uEe/0pjhulT5R8n2dSGwEgJbUl9sbERN68V5QCLPnB1G4uxYxve14Hp6m2H6c+Bm/FGLoi5znl5q8f2qIePeuHB/wujwjvjQX4s1Nw9ce4l7fNvFuuyHvbwFgmHgOL8q4zk3Nn/dSiMu/WvF3tTmpJcpj7rsLkXPyo5ijWnFwUyKcDgDJUtQ6WdyHSt2XhE+XFY83ZRnPSyfGnYj76UQE2yuRF5Q9S/i4J1IvZiOfy33wLzWMMcYYY4wxxhhjjDHGGLMX+KOGMcYYY4wxxhhjjDHGGGP2An/UMMYYY4wxxhhjjDHGGGPMXuCPGsYYY4wxxhhjjDHGGGOM2Qv8UcMYY4wxxhhjjDHGGGOMMXtBft+GXb3hHeSki4Qrl6cDV1xHF9XPAWBou2BrxDhuiXo8AFRkfO2G9zGfldT+oVChv1mv4/NmC9q276kZl1eXwfb81Qvatmtaat+ODbVPZFmnka91mnJl+lG0b5o62K4uX9O2V9dX1H54fBpsWcr38frmlto3mzgOAGj7OO68uLe735tNfU3tR6dHwfbO175O25bHD6n95Wu+bkM/BFsi9m8242exyKNPjxPvoxTfPhcL3vf52UmwHZ2f07Zdws/c9SYemGTifl6VYo4Z3+9mmgXbmHM/z0RMmKcH1L7I4/lfHh7z8S3jOABgtYpxZXsXbQCwRfQFAMgrvmer7XWwff7Jn9C2u7De8hjbjDGmi3SBSsSCjpxtAKjb6DNdz30mSXjfeRn3e+r5XNarO2q/W/P2by5jrB/Ac2JecL8r5tFn5gdL2nZ5yH306Ii3P3p8FmwfffQL2vaXP/85tf8n//F/SO1//9/4R8G2WMYYCQBty3367i6u9+s3L2nba5FzyjnPz/MqOmHy1/B/PrqOz20Yok+rmJ5J3+D7zfqZxLnYqnNLarSm3fK2B3Nqf3x+Qu1nJ4fBVuQ8KNQ1HzfSKpiyBV+PseDjq8HXlaWGUtSbEzdjdhDPFgAczWINdL7knSQjr3XKRfTptIjrAQC//PQZtV/d8PzSDsRfk6++jgKAaeSxMCHHoCj42cg7sQGiFsiK2D7P+DjKgseDOamxEnHXSck5B4BEXI2qZdzbGclRAABRq3d9zLldxx8orjrIEfsAgG6MOXcUsSUTOTfLRUwk/rDZiJiz5ResWR77mI64LySijuyE7yRk2MPA72i70L78lNqzPj6rmvFzv37F1+dmw8e7IXu1nPO+K/Cz2FexVrkTd/0ZuY8AQDLjsXp1F2upRcfncv7196n9e7/z28F2+1MxxxWvM6qcz30itau6Bz/b8vPyjHeNaxIQRxE7L2p+bieSv04e8TtadcrvL+XDp9Q+J/ZZLWLWDvz8z/+I2ocmrufihNeay6dfo/Z+xf1xNsYatJ74GqPk9QeS6GODiLuJKkFH/odpiDXCMHBHSjK+Jyl5ccTqUwAYJj6OQcT6oSfxmId/JAOPx9niMW8/xfPfgceEntU1APppFfvY8rrr9z/6JbX/4ibGJgC4uHwV+xbj2IU0E/tNQsSU8H3tSW4BgEbcD9ar+H5OvTMuU+4zZRl9phe5ZX7Oz/NTcb9672vvBNv3fvArtO3xaazJAaBn75IH8WIXoh4p+dwzEtMLcc+bUlG7sUL57b8gwxPjFjVQRgJUJupQ9g4FAFIxvnFiY/nyd2//UsMYY4wxxhhjjDHGGGOMMXuBP2oYY4wxxhhjjDHGGGOMMWYv8EcNY4wxxhhjjDHGGGOMMcbsBf6oYYwxxhhjjDHGGGOMMcaYvcAfNYwxxhhjjDHGGGOMMcYYsxdEOXfB5ZvX1J5msYuyrGjbJI3q9gAw9h21r+/WwbZa3Ym2G2q/ungTbIX4lFPk/A/LwyW1Lw6Pg+3g8JC2zTK+Jv00RptYj3GMbQGg77iS/dDH9sPA247jRO03l1fU/vzTT4Lt9SvuI6rv6+vrYDs4PKVt66ah9iktqD0r417mZUnb7sLq7obat20bbLPFgrY9fXxA7cmcrwVzg4n4EQCM00DtWRr3ZJy4/6svnwcH3KePT8h8yhlte1Mrn47rN/TRBgCL+ZzaKxGH+iEJtq7hs2zb2BYA2pHHspRMp1vxcS9THn63bTyjd1se34oFX9eu4zHk01/+MtheP/uUtt2FTb2l9m0bz3GR87VMUr72acHtEzH3ImbmJG8BQDmPPpNk/Hlvbi6pfdPyM9d1ce7LoyPadp7xPHJEztZiyX2gmvE5FiIBPnh4Hmx/++/9Dm272cbcDAA///AX1F7OY5x+8s77tO3INhLA8xcvg+3Tz2IeAoBSnItvfPMb1D6fxX2vN9yHd2EQuXrsYjzOwfNmVfKcNyx5HmnzuJ59zX2g77nvshS+3da8bcvti4rH49OTeAYODvlcppSP+/DkLNjSQtShEPGDLzcGkkPTip+t5YLXiienJ9R+mEd/WBIbAEwtP3PdGNfk7AGPe2/ueC66uOO+Pq5i3pnA+96VQeSshNQlIiRLeydq0K6Oa92LmikRhdDQxYcmwpkS4b9Dz9vfbuL41jVvW4p8OY5kPgWvhfOC5+Ki4DFnHOKe9SP334LU5ADw8CzeowAgz+MZa0V8mrXcd47O4jM3PT+7q47P/U7k86aOzxwyEUR24OInP6b2y599HGw3r3lNcvGa31Pe3Kyovc7iWpwd8ZokEWfrNam9mgs+jnzDY1v9+pra+08/Crbl1/4FbVscnVB79zq+G6if81q43PJ1WpLcCgAYo/3V1TVt+pzEWAAYMnEnIfWvim9TKe4YZ3Evn4ja6Oidx9Q+O3pE7fki5vPsqy+l8GefPqP2q5voS2nO49fi5y+ovVvGWhgApjquZybuI2L7kM9jjVAnIr6OPK4NLfeZcSDvSXo+kETcPweSu8aB1w0YeC2QpDxmgtQqSa5yER9fdcTzRUHyXzaJGlX00d1F3y1FPdDMeB9XAz+L2zTWo6XIibvQqdqDxONtz3P15Q2P0414DzEQPy3E+7as4vaBnaOcz+XsPRGTHvBz+/7Xvx5spw8e0rZjIs4zOdDVxOeSkPdrAJCxF0RvnxotImYh4X0M4r44Ef8dRK0IUnMBQErsibhfTYl4nyNyZUrGPYkzdB/8Sw1jjDHGGGOMMcYYY4wxxuwF/qhhjDHGGGOMMcYYY4wxxpi9wB81jDHGGGOMMcYYY4wxxhizF/ijhjHGGGOMMcYYY4wxxhhj9gJ/1DDGGGOMMcYYY4wxxhhjzF7Apc4JF68uqb3r2mjrO9p2HL+YonnfNcE2iL7niznvZIr2PuMq7F3GVdt7oSq/aeJY1usNbbs8OKD2JI1b0PVRrR7gaw0AQ99Te9/F8an1a1vex9Ul3/f1/6u9c/m1K7nO+6r9Pufc9yV5ye42lZZiWIAMGTASZJBZZhnEgEf5W/J3ZZgYcQzYsmxZEWyr2w+JISl183F5X+ee5967qjKgZ+v7LLJPO/ABvt9wsbpOPVattap2X3yLe28k+1sU7NuZ34eiwnvQzY6hPYUG26Nfw7KqyTi+OW9fv4L2n/7lj5ytmh3BtvX5b0H77db7v5lZYX6N0ojb5oj3NQS0zvhcVIGc2yLh9rX36fVA+gjkLA5g3KSL3vC+thH7XRz8uJcLvH49OS8WcOgswHTCFq/TgpyXIfr2qTqEbQ9mM2ifzxfQ/nc//5mz3d1cwra70Gc8ZxRLE4hTZjScWG84PubS/2bO2L9Che1t58/FagtinZm9ePkM2jf4yNl04nPR+ckZbFvUOA720a/VELHv1hmfiyHh9bPk1+TTz3Bs+v3/8O+h/cXLF9D+9SsfJ79+/Qa2XSyW0P7mzTtn26zWsO1n33kK7TnhzUGp9X4xh213g+VIb4sR+yjLYy3wLzMzC/5clCSmt7gHM1CnkNBtmZzn6zlez/S176gqcXwdBuy7Y/YLWB/g2qCpcN/BcMxqa7/eR9MOtj2e4t+c1XixuuDXqjNc51nCsb6a+tqymOBaI9fYRyLYXzOzr772Z7Qne7ArIeN+kT0kHPPItlgzwWemBgmb7dWkZvP+8PM1ZrzOPZs7yGnNhPh1je0VmM/BDC/UEVmnkoxvPPMRoyb56PgY+97JCfbVqvZneuxxbNky++jXb04S9P0Cn7vbOzyfm1u/JvPFx91xP4Sf/cXPof0f/uG5//3VBra9BvchM7NlwjEvgIR0MZvCtiW5r74Gd2E2voMeZ56C1M43t7fOtnqOa48p8fUpmGPY4vEFcj+esjMH7ldXK+xHRt4dnpyfQvvDE38XyAn7f0Vy3eyTz5ytO76Abbv2HNqLgPNRAeqWksTUXZgv8XldrsC7R8Zt52u8r3GC26/AXXNscA3azR5A++TQr31B6p1+xHfYkdyvSnBpCjXZJ+x2lnpwBjKOa+R6xf+hAOMj73FliefOfKlu/N50E/zuNpl9B/cB1vX4CK/fg0f4TWra4oVdg3hYjqTW24GexHQDb0FrUqtvIh5XAvtnZjY78ut8OKW3CcgAajpSdtjRIxwbDysc60+ePHa2WOJaZ7Vh92lvI6HbMjlczB6TX++R1LjwsmhmFbFHMG62j5m8OwTwNpjAvdLMbGSbBt4tzcwK9O7ILpcfgP5SQwghhBBCCCGEEEIIIYQQe4E+agghhBBCCCGEEEIIIYQQYi/QRw0hhBBCCCGEEEIIIYQQQuwF+qghhBBCCCGEEEIIIYQQQoi94IOFwgMR7kBCrJs1FjhZr7EIF9EntgKIanYdFp85OT6B9rMzILBLRJaGHovj9ERMZ7Xx7ZnI6e0dFsmsaj+fRMbHBMEzEYFLQBgoMxEhom83bPCeIRGnCRGSKyosplZUfu4ZCH6acdHgQAS2iuD7YaJpu7Ba4f3++tdeRPjLL/4atn36Qyyg2BMR9DD6A5OJqFBFjnhCh46JgWUiwkhE1u7BFjYNETIH+/T+N4F4LBHIRUJIZmaZBJYRnIENEQIcgNCkmVnTYNEjNEb0e2ZmQ8J707T+XJwScdApEXZ6/sXfQ/vLf/zS2TZEGGsXqgqPqwRiViMShTezkRz6RIJVAOJXVYX9qyFic0ibbjG/g23Xa3wuiG6mzQ68mFoaiOAt8a8tEF8LRDgdiX2amQ0DjukZCHzdk3xWEkG2psWx7OtLL/L97hIL1N/d4lx5e+Ptwxbvwdu3V9D+/NlLaEcbn0ketv+GzR9CIiLtCcS7TMSGE6nFAhNWrED7hNuivGlmVoC4VjGVvJHkWSIwfX1942yRBPWeHK4++bGsMz63wwYLe44DEfwEMfbs7AS2vXiIBQwvTvC5eDDzfZ8QfUWiP2kGzmJ3gH3k7BzH2vPbBbQv1v4MrNZ4nXalKfDY6tqPYUJE3c/PsKjn44szaJ91oKYgdVBFxBVrIHQaSB+3d3jt3l3hmDcB4zs8xLEX1nSG7zVhxONY3XkxUzOz1RL7x2p+62yd4Zw2JyLkbxscL0YgVrrdYh9ZEgHqt3M/n6t7vNarFRFDxynGcuHHXXbgvrkj45QI0j554m2kZpoU+LzMyd22APXbbz3CwsdnRPx0fn3rbCXJLw8/9UKuZmbTi0fQft37PVyO2O+6Y7wnD2a+Hiu2+FwkUiNPprjvuvF34cU9zkfjgN8dTp98Au2zh35NBpK7hgU+t+XUj6+c4LgykPqyDth3UN1UZnz2d+EQzMHMzDZ+XOwex4R7l1Rs2e9VFbBvHJH/ZXgK3kkaIji/IPtH3wZALC2IiHwacczstyBfACFjM7NIalQmFI7KzphwTO8j9ulYkDjdef9NAcfOIeAiq2v8uk4n+Iy35w+h/fwE+2UHYuoRe3jbgRjZufTrxkSdC7AOZmaTAzy3o0O/Rqi0MjPb9nhfA6jtSbqwSYPr6eYI13nTI1+Xr3t8bocNroHQ22lF7lwFEdFmuz2CM5AM72Mgb6T87dT/KjtzJOpZAe6c7J16ID5Fxw1iSBHYZec3o7/UEEIIIYQQQgghhBBCCCHEXqCPGkIIIYQQQgghhBBCCCGE2Av0UUMIIYQQQgghhBBCCCGEEHuBPmoIIYQQQgghhBBCCCGEEGIv0EcNIYQQQgghhBBCCCGEEELsBdWHNmyJ2nxVtd4YsOx9PcFq7mWJlc67tnG207Nj2PbpJxfQfjjz47u+uYJt311dQntcbaC93/TONgKleTOzlFbQXpV+C+oab0tZ4m9QMWIl+9j78aWI96Ao8B5E0r6q/BjbDu97Lvw+mpmN0c9nJHPJOWB7TNCesrdnw3uzCzHhPldLv9+/+sdfwLbTs8fQ/vh7vwvt5cSvZ4p4jRPwATMzsDw2krXMqLGZpQjNFoet79tIH+S7agIxJAd8LmLCvmsB27N5H0vEN3LAftfUeNxt58dYRNJHC2Knmc3AOTqqyfpdXUP7lz/6H9A+v37lbOOIz/guJJJaygb4KYlrJdmTosdrMYDQwXy0xKHKCjDs7XoJ266u76B9DfLC+75956+/egnbpogHPp10zvb5089g29PTUzy+Ld7vu8W9sy3meI6bAc9xscRrdXXpc+v97Ry2XS7X0L5a+Jg6kPg2v/dzMTN78/oNtIfC+2BZEyfZgUTiYAZHIJFaIlQ4nhCXsQTmZqTmMiOdBG8PJDaWJDZaSc4z+Mmxx3WABdz3sPGdXL/6FWz75qvn0L66wr6Rk9+zssK+UdX+fJqZdUcH0P7wyZmz/dunD2Hb8wOc4wNYEzI8G9Y+N5uZvbrD53w++FgxkHpgZ0ZcZwew/kOP53H5FZ7H3SXe2/XSx4m76xvYdkNq4YMpuBuhZGRm1+9wvn771udlM7ME5p5IPdGDvTIzW218TM6kbo1bvK4xEnvvY3Id8NwLUttvexzvN2CPs+G5M48cwT8kErcCuQN13Qzazy983v38t39IRvLN+cF/+o/Q/juDn1x7gO/pI7gHm5kNI473AdTf3QT3UZE+xoXP7+gsm5k1h3iNqxMfH83MEsglIzmf7N5Xg7eLQPqwkdR04P7+3u77TqQYjaSWYveXqvO5JJNaYdzis7VeLvw40GExfgdKCZ/nMPh5jmTfd2F6eATt/QaMC4zJzGwENbmZWSLvDRVY55acrc+/+xTaH3zf269w6rPLG5wvNiv8nlSgu1TG/pVG3Md25v1uIL4xkDwykpiw7UE9Eck9nb0FgfcFM7MIzvmW5OEh43Mxgphaz3Hbd3d4HDlh3zkFfjIF7wW70rM4CPJyJnVid4zj8aTF/0EL7Dnj9RmBD5iZZeC7TYfr6brGea4h+S80fk8KcsYb8o6ZRr9+MeBzEUmdUpL7VQT3KzI8KwryFsqudOCIBsONSxLrC2Bn91PLJNZmkitB3xXJfR+C/lJDCCGEEEIIIYQQQgghhBB7gT5qCCGEEEIIIYQQQgghhBBiL9BHDSGEEEIIIYQQQgghhBBC7AX6qCGEEEIIIYQQQgghhBBCiL1AHzWEEEIIIYQQQgghhBBCCLEXYDlyQCiwGvmkO3C2dnoC2+YSK6tPJlix/hgo2XctHvLJIe6jbfy4F4t72LZgcvNEyb4o/VjCMMK2OSVoT+bbl20N2zY13oP1sIX2zXrtfy8OpG+mTI8V7uFYCvyNbBjJ3Ec/94r0EQq8JhVZk5T8noXA9vebkzNen2Hone3qza9g2y9+9MfQvurxWjz9/g+crcXLYJt+Be1xiM5WBLL22bc1MxsD9pkIfL1I+FxEsE9mZmPy6xoznmQL5mJmVm3xb242/gz0W79fZmYpYvu2wL9ZV34+s+kxbHt4QM5c8n3PX38F2778m7+E9i//9sfQvt4snS2R2LQL2xGfi1D7c9xMGtwJiT0l2e8N+M1hQfYv4j5asPY9ia93d3Nov7nB9gR8OpAz1G/xb5YgPn718iVsy2LT3XyBfxPkroLEzJrk4UDicRz8mRvJmYtgD8zMQunnXjY4L2TSh5E1+fZPACawOgD4Bvs/TkjItLIiuXD0exUjy5t4fGj/Elnjhoyv+IhVDgVuC1zg/W+Wfiypx+dwffsO2lfXb6A9gphpEecWw+5locJ++u4XU2d7/eQCtj08bHHnwHfKFredTma4i4DrwhHEVFYP7Mp2BdbZzOZzb7989TVs+/LZ30P79dtfQ/vtle9nvbiBbSOpYUpwD2C1FItLI/EnFMNZXGcEMBY0ZjOzsiD2EseLqvCHvSFxiJ3/YcS+h2JRWeHxZXJHG8AdI7H1I3cPCyRWgnVtp/4+vCsHjx9CewbBpiD5MHV43RJ5AkjgLjyCO42Z2XqJz22u/P7VFV7jjW2gvVhfQbuBebK6Ad3FzMwM+FcgvkFKURh7zXCZUZAzxO6lqCYwMyt6v1Z1RWpoMu4BxDJ0VszMMol7rJZB1EZy1w4syB1jU/nfWmU81vsR+wyzDyCGtSAGmpmdnuJ73yePvP044/U5PsLnebPANfw4Al8n+Tpl/GYW06k3krzQR7yu1zfX0D6/9/eamHHfA7nnDWscbwZwLuIax5WY/NuYmVkCOeA24/yUSd56R15VD6f+jL4FNjMz+8+/j+0fQD/gOWf0ZkFq9ZrkkZa8Tda1X7dtj89nZPcDUCPXpI4tST0dM45VW7AmrG1N5pjBb6Ic/N6Oz0UgtUQB3sYTibssqGf2dgrWm6RhWuug30yRzR3bRxKHAliqTBPub0Z/qSGEEEIIIYQQQgghhBBCiL1AHzWEEEIIIYQQQgghhBBCCLEX6KOGEEIIIYQQQgghhBBCCCH2An3UEEIIIYQQQgghhBBCCCHEXvDBQuEjEcCeHnihm9khFkjKRFCpKoggLBBPuZ9jke/VPRb3axo/xeX8FvexxKLKDRC3NTM7OT5xNiZCt2WiqEAgkIn0IFE+M7ORiMo2oJ+2weJQZ2d4z2oydyTitO6xcNLlJRbsvL3z612SOdZA8N3MbEvEpMYBiOgRsbBdyKxP4Af9FgtcvXv9AtqHH/1PaA9AKfbp59/BbYloT2HeH9sS7zWjIWpDFRChLAu8f5kI18fR+/RIFHJXK3xuExHfjL2f+9DjM8SEwtdEZKpp/fk6m+C5nx/g9Xv767fO9rM//yPY9ud/9b+h/c0NFsOF4oNEqHAXVhvsdz0Q9yuIjyKBUzOzFIk9+XXe9Dg+9EBUzsysar19MsF7PRKxcZYrRyA+mA237UksRaKSV9d3sO2WiOTdL4iwJ4hZDREE72ZYwK2qiYA4iutEk6wiYrAFEiEntQPLF0w4E2le1vW3L25JXBcOiwl3GhGQS0yIG4iucSE27NNI+C6S8fVkHOyco8UfiP9nIp4agWDlpCO+McW+UYJa0cwMpahIRCIzERg1kkfGCPIcWb/7+w73DdavIoJ/B+cPoH1C6r8SiA8WHyEQ+zFsVnhNN0sv6rm8xzHsfoHvB4slrkF7kPeZ4DDzvQRieEWEewtir4jwZfqImpXVWCGAeqzEgagh42iJYOcE1DsHE+ynBYk5AxK3NbOm8/0cn5zBtqyGuQKCtfN7LLIbSX05OQTCuWb22effd7ZPvvNv8Ph2oN9i30UavWEkwt8D9rsIBMHNzDYrXzuwmikQAdAWdB0C9q9AioFE7nfoJ8cBn5VEfAN5Y0XWg5EzqUWRjaRz9ous/oUpmuxBJPkIirOSt4s04nXN4I5mZlaA+18fiFj7Drwi7x534K1l1ePFX5E6Y0tqewNxM69wLnr+4pfQPk58rs31FLbdbLCg9bDGMWEAgsirFXkzW+Jxb3r/m+yKuCB377srLBS+Wfu+S1JnhwrnkWFLxL9BfGJnK5Mcj87c8g2ujVZHOC+wdyZUXr4C76fv+a/E/pvZkHMJ75pkrG1F4jFxhBGs9IaIyI8kxrag9ggglpiZ9eRtpx+xr9sWnCMirF0V2B8jmOPI/KggQuGkPbrrDSy+knvUlIy7q72PFUxUnNgjqPlTJm935H4QwdvP+x/17ccd/t5Cf6khhBBCCCGEEEIIIYQQQoi9QB81hBBCCCGEEEIIIYQQQgixF+ijhhBCCCGEEEIIIYQQQggh9gJ91BBCCCGEEEIIIYQQQgghxF6gjxpCCCGEEEIIIYQQQgghhNgLsLQ8YDGfQ3vbds52fHgI25ZFCe1Dv4H2+dwr1t/dXMG2aeyhPRRecX21XHxUH48fP4L2Tx89dLaiCLDtLVm/+/uls623WPV+0WO1+S1pX5V+vU+P8N48ubiA9q5toB3tZQp47s/at9C+WT93tmHEc0wl+f6WcPscvT0H7H+74f2LWXOKsG3qsf326iW0P/s//8vZyvjvYNsHF9h3Dw6nzjZt8F6nlKA9kP227OcTR7xOQ4/P3LD1fQyZ7HXG4xiI71bBj6Uh7lW0M2hn5+LkyK/r0QSH2e3iHtq/+MmPne3Hf/ZHsO311Wto78cR2kMBJprw3uxCTmTOINQHEjPrCp/Xsqyh/fjY70nT+v0wMysC3vCjwyNne/jQx3kzs/OHj6H97hbH+sXGx+nNCue+q3c30D4O/rxkdg5B/Dcza4jvGuimm+C1rlpWOnyEL5FhkzLBqtaPpZnicTSgrZlZCeqB93b/o005wQPZgUDWJ2UfYzNrS84r6uP9j3o7WwczvPgJnNFAxgHCq5nx/BeBPYIcYmY2kvqgAGdgQnzj8OwA973xZ9/MLJV+QukOx9cc8biNzB1Nc2D1KamTA1jwitQ6ocE+HaY4Tk5mfq1o/NiRkfhTAeL98ekZbPu0+D60nz7GsXqzuAM2nJfLhPe8KvwmTrsWtu3Y+pNYnc3/Zk3yZVX5u5iZWRz9+Y8j9iVWC8ymuA46mB2Dth9XR/Y9vr9MjrzvnZ2fwrZVheP9zdyfJXSvNDMb8PZa2eE9Ozn3d6bZ5NvPGVty74tgqwJJqtFwH8OI49IW7MlI7sdNjeNsrr0f5Aq3jSTXRVLHZlC+RZL+Esh/ZmYlXCq8fgX5/z/Zb6L8VRIfrcjddiS5ZBj8Xg4gTrwHzyeBO1MKZG8yu+OSPQNmWm7swJo8X63AnmzYHZHcAwpyF65B7iuBn5uZXb9+Ae2L1crZQoNrkn7AsXHc4BphC+z3d9ew7Wbtx2GGz1zO2L/Ye81AYhZypUBiQlFge2L+COysD/Z2ge6WKZC6tcSHP6M7tpkN4BAwH96FGNndH9wDyB2MFfEjqWP7rd/v+yW+B4+gHjEzK8FbcmG47QI9JJjZ7RLXbhHEwZqc25LUUT0I9vcbXEukRN6jie8mkIfHiPuY1DiPPDrDtVF37Gvlmpw5I/HQwNsbuenAc2j2z51b769FYo75m9FfagghhBBCCCGEEEIIIYQQYi/QRw0hhBBCCCGEEEIIIYQQQuwF+qghhBBCCCGEEEIIIYQQQoi9QB81hBBCCCGEEEIIIYQQQgixF+ijhhBCCCGEEEIIIYQQQggh9gIige65ubmG9nW/9bbNCrbtWqzaHoD6uZnZMHjF9e0aq82beWV6M7OUfB/r9RK3HX1bM7P1Cs9n6DfOVtd4Se/nd9D++vKdsy3u8e9tezw+y3judePXu67wd6zy7SW0FwVWoZ90nbOdP3gE256dHkN70/q1Wiywn6WEfSTlCO1x69dqLL79b3hFKKE9W/4gm5lZztieYg/tN2+fO9uXP1nAtqcPP4H23/nh7/m2T78H28aKnM8t2ZMRzYesPZ66lcDvYsSN+zWee9pi3+2A3x3OvD+bmR0dtdDetg3+zXHubH/3xQvY9hc//ym0/+RP/9jZrt69hm1zwv5fBhyHAvDXf4FjYU8++S60t++m3oi3yY5PT6H90SMcZx5dPHG2GdnXQPLFuPVn7vgIj+Pg6Ajaixr7xgD89+oKx7uf/vSvoP2Lv/5bZ7sjubklsb6e4PGF4NekJW1ZztmC9Xvf3Ldn8TARfwilb58C7mOMOFeOCbevQWnSdCQ47cCwweNKhV+fBNbsfVsWd7E9gHUmacss4MUvQalYsOAdcR8p4vlEEMIi8a9I8v0Acmgu8PgOD3BMt4f4PFfH/gzcV7jvLTnPaevrZDMzA/6bIm6bo68333fh1ySSoJorUq8UxCHAXoZugtvuyLrHaxqjj2NVDfKImZ2c4TvG+QWug4bBxytWd9Uj9r0w+vtEkfAeFhW7A0GzWfJ7TsK6VQ3elxbko4LdGSpcNzTTGbRPWr8PBcgjZmYRHXTj95pm6s9pWeLJFyUe92nlx9d1eHyZBMVEYqKB30wbHIN3IZI7GNxCUvdZwnZ290CFeWF4fYJhn0brOSa8fyzeD/2H58BE+sBWs7r042O1h5F1GklMGIC9nZC9KcjekDtTBnk0sVxMyNlPNJH9jeTuPZJ6I5m3s3vyLnRHZ9A+K3y8azMJmiWub2eHJ9A+nR58cB+BXaxQXZJxbs8kMRQNud8lcObYu5vhmI5+swD1qRmvUSNZb3SMAomvBVs/cG7f9+3tIZB4A/I+o+3wOs2OsL1ElwnD75wVie07gUOSRbBXI4mOG5KTA8m/I3gLWtzjdxn26INyeENqhpsFfiO9Ju+sGfhS3bBzi9+YV8BnlivcdiBvyQWJ6QU4A5m8i481ftM4aHDN2Xd+LAXJOYncmXoQ64eIx5fJHY3Fw4ju5KDm/1D0lxpCCCGEEEIIIYQQQgghhNgL9FFDCCGEEEIIIYQQQgghhBB7gT5qCCGEEEIIIYQQQgghhBBiL9BHDSGEEEIIIYQQQgghhBBC7AX6qCGEEEIIIYQQQgghhBBCiL0AS6ADFkusZL/abJxtvcTK9HXlFd7NzJoaf1tpGz+8tqlh26rCfaQAVO8rPO3tgBXr53dzaH/27LmzjRErv19eXuG+F35dU8LrlA0r01dkPgHMfbFcw7YJKdCbWUoJ2mfTmbMVZQfb5oDHV1Wl/70R70Gf8fiy4bUah9Ebi2//Gx5aYzOznNG64TkwMln7ofdn7u7qDWy7mt/iPrbeD8YVPrenD86gPWQ8924ydba2ncC2bYN9pu28b/QD81FsH/oe2i369gm7nfVL/A+3l0to/9WL/+tsX/7Nz2Dbd69/De3L+1tni+RcBOz+ZgH4v5mFwp/FEth25Q/+yx9C+2Ll120kczs8OIL2i4cPof3k+MTZOpBDzMz6AfvGCuSuqsR+Pplg363bFtrR2scR79Nvf/d70P7fQdz98z/5E9h2scB5C8VdM7Oi8PYMzoqZ2YDiq5kNPc5/KEyT0GmBhElkHwYcI0diZ533vW8f07efLxJZt7EC4yLxP2S8xsFwezgLsNdmZoEElAxiLMt9xnL1R9Q1LPdFUl8lEMBTxGe8meCYcFzjPDcp/fiamc9xZmaLo1No39zcQ/sA6uq4vINt84jnk4E/BFIXDUs8jk0gMQHUMRHk92+Docd5YARbnjOpeUtiJ/eDumhAY+x75UjOY/TrUZE+CpKvg2G/LpLfczIVqxqcdyYgH01bMG/juatqca5ran8fK0mdi2tiszGSWF36xdqA+6YZjt9mZkPy65pInExkHImMOwF/WGV8v9qFgcRTA/MoM8kvNG6SuQE/HZnvkvUxsJ4F2NP3Tdn+kZof/CbzL9IFvGfTcprkxQEFJ8MpsCDjqFgeJfd6lNDZ2wBznRGMO5PGW1Kfs3g9gHcUtn678Omnn0P7CtTUA6nlYoHfkyZTnONKEMBHUtewHAxfBsgdDL1fmZnVoCYxM6uLc983OfvbSMYHclfX4vqgYfcrcg3Yojs5ObcVqUnIsFG4sYHk7PUdfs/cgvxSgRxnZjaZknseeedEA6T3lB2YkVw9ZH8uB5LzcsZrT9cCvOF29QEeINnvduLXM5J80TTY/4+P8LjLxtc7FXlLZv+v/xGIj33EMZDF3YKc5wbtGbnndaT2nZF8UZdonnid2HxQDsjEd1ioZ7dplBdpTvwA9JcaQgghhBBCCCGEEEIIIYTYC/RRQwghhBBCCCGEEEIIIYQQe4E+agghhBBCCCGEEEIIIYQQYi/QRw0hhBBCCCGEEEIIIYQQQuwFH6wQm4mQ3QiEeIiWm0UioDOORNwP2JoKi7swcVEkflqTPlKN5xiJWOrNtRd03BAh0OWKLAoQRGk6IoZORBBbIvo3A2JX3QS3LYlwKBPsRKLbqzWeYyaiNE3t5zOZeSFcM7NIlHeYUGwAwqvVv4BQeElEhA2cCyZqTnTGqNgQ+oeUPk649wqIVL8gftRvPoP2k1MsrIr0iqZTIhTeERH52otGjUTxLxKxvu2WKQQCIbk1FlC9vCb2t19D+7Nf/sLZbi5fw7bDFgu/IlE9FMfMzAqiylQSkbUKiEY1Nd73XfjB97DQdQJnMBFHn3VY8Kwj481wLZjPYJG3ae1/kwmFlkTEjAlxo/gzBuz/T8684J+Z2e/97g+c7c1r7IvPf/kM2pGQo5nZ0G+djYlvMqGxQNQwA9gHKljekfVr0VoRIdGI4yH2EYPqZnFkAfibU45YSLYI/lxmImhbkTVO5P9RQeLELK0bECY2w0KuTFiXxeMIBDzNuOAcoiA5PAB7R/JZIHYmlGqgbp11uE5ZPXwC7XeXc2hfXN/4Pt68gm2HO9/WzMwiqruI8DRRmaZiv0icfCAqoDuSC3w2goGzQcquEooicuHBEghfMo1CVFOamZXB1zYNEwonOaMmF5gK2EHZ/E99439Ad6aWiGQ2LVm/Dp8ZJBQeyDliotRMnTmiei/gccToc5eZWQD+m+zDY9w/9UKswFFI/NyFFRFqrkFNmIAw+nvIuSdzK8E654L0zWIy8Ed03sy4z1QNrgEzOKTMv6gQPZh6QepmWmeT9YbvDg2uOauS3L1JOsrwXBABdiKUnEFcGYif9aSPvmd3Tt9PJPloFx588hjat71fiw25B2+2eA6J1F7IT2tUYBnLwAaLr0zqVVTXmBk9c5bB3ZHkxCkR+UauVJCak70blaw9ElsmgvOJvKVlFuMyaE/6KEicRvZAhNYzGx9RMg8gtpTEz3bh4uAC/0Pnx5VI/GdvjTW5ezeFj22J3M2YADYaSo/21Mw25C4xkL4z8FN2Ptl7BDharHSB7yxm/O25AY9mPZljIDErj7gGSsnbiYvaQN7YavBOgdbUjIuhl6wGKdFOfPCnCf/73/i/FEIIIYQQQgghhBBCCCGE+P+IPmoIIYQQQgghhBBCCCGEEGIv0EcNIYQQQgghhBBCCCGEEELsBfqoIYQQQgghhBBCCCGEEEKIvUAfNYQQQgghhBBCCCGEEEIIsReEnInUuxBCCCGEEEIIIYQQQgghxL8i9JcaQgghhBBCCCGEEEIIIYTYC/RRQwghhBBCCCGEEEIIIYQQe4E+agghhBBCCCGEEEIIIYQQYi/QRw0hhBBCCCGEEEIIIYQQQuwF+qghhBBCCCGEEEIIIYQQQoi9QB81hBBCCCGEEEIIIYQQQgixF+ijhhBCCCGEEEIIIYQQQggh9gJ91BBCCCGEEEIIIYQQQgghxF6gjxpCCCGEEEIIIYQQQgghhNgL/h+jENoJvRaVxgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x800 with 32 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set the model to evaluation mode\n",
    "model.eval()\n",
    "\n",
    "# Sample 16 images from the test set\n",
    "sample_indices = torch.randint(len(test_set), (16,))\n",
    "sample_images = torch.stack([test_set[i][0] for i in sample_indices])\n",
    "\n",
    "# Move the sample images to the same device as the model\n",
    "sample_images = sample_images.to(device)\n",
    "\n",
    "# Pass the sample images through the model\n",
    "with torch.no_grad():\n",
    "    recon_images, _, _, _ = model(sample_images)\n",
    "\n",
    "# Convert the images to numpy arrays\n",
    "sample_images = sample_images.cpu().numpy()\n",
    "recon_images = recon_images.cpu().numpy()\n",
    "\n",
    "# Rescale the images from [-1, 1] to [0, 1]\n",
    "sample_images = (sample_images + 1) / 2\n",
    "recon_images = (recon_images + 1) / 2\n",
    "\n",
    "# Plot the original and reconstructed images\n",
    "fig, axes = plt.subplots(4, 8, figsize=(16, 8))\n",
    "axes = axes.flatten()\n",
    "\n",
    "for i in range(16):\n",
    "    axes[i].imshow(sample_images[i].transpose(1, 2, 0))\n",
    "    axes[i].axis('off')\n",
    "    axes[i + 16].imshow(recon_images[i].transpose(1, 2, 0))\n",
    "    axes[i + 16].axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fuyu",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
